pronunciation patterns among l2 hul’q’umi’num’ learners

Pronunciation patterns among L2 Hul’q’umi’num’ learners

Sonya Bird1†*, Janet Leonard1†, Tess Nolan1†,

1 Department of Linguistics, University of Victoria.

†These authors have contributed equally to this work and share first authorship

Corresponding Author [email protected]

Keywords: Hul’q’umi’num’1, Salish2, adult3, L24, pronunciation4, errors5,.

Abstract

This paper reports on a study of pronunciation errors made by adult learners of Hul’q’umi’num’, a Central Salish language known for its rich consonantal system and complex syllable structure. Analyzing a set of 2,915 elicited words, across four pronunciation tests and 35 speakers, we are able to identify several factors contributing to learners’ pronunciation errors; some of these are relatively broad (L1 transfer effects; effects of hyper-articulation), and others are more specific (e.g. familiarity of lexical items and sound/sequences, word, syllable, and stress position, phonetic robustness). Our paper lays the foundation for effective pronunciation instruction in the context of Hul’q’umi’num’ (and Coast Salish) language revitalization, and also contributes to broadening our understanding of L2 pronunciation cross-linguistically.

1 Introduction

Hul’q’umi’num’ is the language of the people whose territory extends along the Salish Sea, from Malahat to Nanoose Bay on Southern Vancouver Island and on the adjacent Southern Gulf Islands, in British Columbia, Canada. It is one of three dialects of the language often known as Halkomelem, the other two being Hən̓q̓əmin̓əm̓ (Downriver Halkomelem), spoken at the mouth of the Fraser River in the Lower Mainland and Halq’eméylem (Upriver Halkomelem), spoken further up the river in the Fraser Valley, on mainland BC (Figure 1). Although systemic racism, including devastating language policies and practices, have led to Hul’q’umi’num’ being highly endangered, it is making a strong come-back, thanks to community-based efforts to reclaim and revitalize the language, within schools, homes, and the broader community. In 2018, approximately 100 fluent speakers and 1,238 active language learners were reported across the three Halkomelem dialects (Dunlop et al., 2018). Currently, there are an estimated 30-40 fluent Hul’q’umi’num’ speakers, over 200 fluent second language speakers, and over 1,000 learners of all ages. Many learners are adults at an intermediate level of proficiency in the language, and are teachers of the language as well as learners.

As is the case with most Indigenous languages in BC, adult Hul’q’umi’num’ learners are spearheading the language revitalization efforts, having taken on the responsibility of passing on their language to future generations, as teachers, parents, and researchers. These learners require support to overcome the challenges they face in becoming fluent speakers (McIvor, 2015); the goal of the study documented here is to contribute to support for their pronunciation work in particular.

Hul’q’umi’num’ L2 pronunciation

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This is a provisional file, not the final typeset article

Figure 1. Map of Hul’q’umi’num’ (right) and Halkomelem (left; regions 5-6-7) territory. Sources: http://digitalsqewlets.ca/stames/maps-cartes-eng.php (left) Hul’q’umi’num’ Language and Culture

Society (right).

In terms of pronunciation, mastering the Hul’q’umi’num’ sound system is no easy task. Hul’q’umi’num’ has 37 consonants, 21 of which do not occur in English (see Table 1). In addition, many Hul’q’umi’num’ words contain sequences of consonants (clusters) that are difficult for learners to pronounce, e.g. the [xwt’w] sequence at the beginning of hwtth’xwuw’i’tst ‘washing someone’s back’1. As emerging Hul’q’umi’num’ scholar Rae Anne Claxton Baker puts it, in talking about her own learning process, “I sometimes went home feeling worn out and my tongue was literally sore from trying. But my granny is right when she tells me that it’s important to work at the sounds of Hul’q’umi’num’.” (Claxton-Baker 2020, 54)

To help support Hul’q’umi’num’ learners in their pronunciation work, we have been documenting the specific pronunciation patterns of learners and comparing them with those of their teachers and elders, through a project partnering university-based scholars and community-based elders, teachers, and learners. This paper summarizes our findings so far, based on an analysis of 2915 words, produced in four separate pronunciation tests conducted between 2016 and 2019, by a total of 35 learners. Documented errors are grouped in various ways, elucidating the most common types of errors as well as possible explanations for them. Practically, this project serves as the foundation for developing pedagogical tools and methods for effectively teaching Hul’q’umi’num’ pronunciation. Theoretically, this paper contributes to our understanding of adult second language (L2) pronunciation acquisition. Lee, Jang, and Plonsky (2015) note the overwhelming focus on

1 We use the Hul’q’umi’num’ orthography rather than the IPA throughout the paper to ensure accessibility to the Hul’q’umi’num’ speaking community. Please see Appendix A for a conversion chart between the Hul’q’umi’num’ orthography and the IPA.

http://digitalsqewlets.ca/stames/maps-cartes-eng.php

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English in the literature on L2 pronunciation. Our hope is that documenting the L2 pronunciation patterns in a Salish language, which differs in important ways from more widely studied languages, will lead to a deeper understanding of the factors influencing L2 pronunciation cross-linguistically.

2 Hul’q’umi’num’ L2 pronunciation

2.1 Hul’q’umi’num’ sounds

As is typical of Salish languages, Hul’q’umi’num’ has a relatively simple vowel inventory, and an extremely rich consonant inventory (Table 1), including many sounds not found in English, the dominant language in the area and the learners’ first language (L1): ejective stops <p’ t’ kw’ q’ qw’> and affricates <tth’ ts’ tl’ ch’>, glottalized resonants <m’ n’ l’ y’ w’>, uvular sounds <q qw q’ qw’ x xw>, and an extensive set of coronal fricatives <th s lh sh> and affricates <tth tth’ ts ts’ tl’ ch ch’/> (see Bianco, 1996; Elemendorf, & Suttles, 1960; Gerdts, 1988; Hukari, 1981; Kava, 1969; Leslie, 1979).

Table 1. Hul’q’umi’num’ consonants (using the Hul’q’umi’num’ alphabet).

Labial

Dental

Alveolar

Lateral

Palatal

Velar

Labialized V

elar

Uvular

Labialized U

vular

Glottal

Stops p t k kw q qw ’

Ejective Stops p’ t’ kw’ q’ qw’

Affricates tth ts ch

Ejective Affricates tth’ ts’ tl’ ch’

Fricatives th s lh sh hw x xw h

Resonants m n l y w

Glottalized Resonants

m’ n’ l’ y’ w’

In addition to a rich segmental inventory, Hul’q’umi’num’ is a morphologically complex language, with many morphemes expressed by a single consonant. As a result, words can include relatively long strings of consonants. Word-initial clusters of affricates and fricatives are particularly common, e.g. sxt’ekw’ (‘carving’) and ts’lhhwulmuhw (‘fellow First Nations person’). A small number of previous studies have examined specific aspects of L2 Hul’q’umi’num’ pronunciation. Onosson and Bird (2019) show that vowel glide sequences are more reduced among learners than elders, possibly reflecting English influence. Percival (2019) shows that L2 speakers’ ejectives are more consistently strong/tense (Kingston 1985) than elders’, reflecting possible hyper-

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articulation of these sounds (Eckman, Iverson, and Song 2013; Haynes 2010). In a study including a subset of the data considered here, Bird et al., (2016) show evidence of both convergence on (e.g. <lh> pronounced as <th>) and divergence from (e.g. <th> pronounced as <lh>) English pronunciation. Taken together, previous findings show that Hul’q’umi’num’ L2 pronunciation is complex, including a number of possible influences (discussed below) that warrant more thorough investigation, and that go beyond what might straight-forwardly be predicted by comparing the sound systems of L1 and L2, e.g. through Contrastive Analysis (Lado, 1957; Wardhaugh, 1970, Oller & Ziahosseiny, 1970).

2.2. Factors potentially affecting Hul’q’umi’num’ pronunciation

A rich body of literature on L2 pronunciation acquisition suggests that adult Hul’q’umi’num’ learners face a difficult task when it comes to hearing and pronouncing the language. As mentioned above, 21of the 37 Hul’q’umi’num’ consonants are not found in English (learners’ L1), and are relatively marked across the world’s languages (Ladefoged and Maddieson, 1996). In addition, two- and three-consonant clusters are not unusual in the language, especially in word-initial position. Based on L1 transfer effects (Archibald, 2017; Flege, 2003; Flege, Schirru, and MacKay, 2003; Gass and Selinker, 1983; Iverson et al., 2003; Nemser, 1971; Schwartz and Sprouse, 1996; Scovel, 1988; and Weinreich, 1953) as well as universal markedness effects (Eckman, 1977; Broselow, 2004), the prediction is that the Hul’q’umi’num’ sound system should be challenging to acquire.

Nonetheless, Hul’q’umi’num’ learners take very seriously their responsibility of passing on their language in a way that honours their elders’ way of speaking, including what they think of as “authentic” pronunciation (Bird and Kell, 2017). They are highly motivated to go beyond comprehensibility and intelligibility (Derwing and Munro, 2009), often aiming for accent-free speech. This motivation can go a long way towards overcoming the challenges they face in learning such a rich sound system (Speas, 2009). Tied to motivational factors are ones related to social identity. Previous studies have shown that L2 learners index their social and cultural affiliation through their pronunciation (Gatbonton, Trofimovich, and Segalowitz, 2011; Nance, McLeod, O’Rourke and Dunmore, 2016; Rindal, 2010). This factor is especially relevant to Indigenous language learners, who are learning their language as a way of reclaiming and reconnecting with their cultural heritage (Hinton, 2011; King, 2009; Morgan, 2017; Bird, 2020). Babel (2009) and Haynes (2010) show that glottalization is particularly likely to be incorporated into pronunciation among Indigenous language learners. In fact, Haynes (2010) finds that glottalization is sometimes incorporated by Indigenous language learners even if their own language does not traditionally make use of glottalization. They attribute this to social factors: learners are emphasizing sounds they consider to be markers of Indigenous identity. Bird et al., (2016) show that, in Hul’q’umi’num’, learners sometimes pronounce <th>, which is also found in English, as <lh>, possibly because, similar to glottalization <lh> is perceived as a marker of Hul’q’umi’num’ identity.

Relatedly, hyper-articulation in pedagogical contexts may lead to over-emphasis of certain specifically non-English sounds. Saito and van Poeteren (2012) and Uther, Knoll, and Burnham (2006) have described the phenomenon whereby teachers over-emphasize sounds that are distinctively not part of students’ L1, in an effort to increase their awareness of these sounds. This can lead to over-using sound structures that distinguish the L2 from the L1 as well as over-emphasizing their phonetic correlates, something which Bird (2020) has documented for a neighbouring Salish language, SENĆOŦEN. This idea of hyper-articulation aligns well with previous work that has shown that paying explicit attention to the linguistic differences between


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the sound systems of a learner’s L1 and L2 can influence a learner’s L2 pronunciation (Guion & Peterson, 2007; Schmidt, 1990).

Beyond the general effects described above, acquisition of L2 pronunciation has also been shown to be influenced by the details of the specific sounds and sequences involved. A segment’s position within a word or a syllable can affect a learner’s ability to correctly identify it. For example, Japanese ESL learners are reported to be extremely accurate at perceiving the difference between /l/ vs /r/ in coda position but not in onset position (Carroll, 1999). On the production side, Gonzalez (2008) shows that Mayan learners produce ejectives more reliably in onset than in coda position, something that Archibald (2009) suggests is attributable to the segment’s phonetic robustness: ejectives are cued primarily by a release burst, which is more salient in onset than in coda position (see Wright, 2001). As far as Hul’q’umi’num’ goes, Percival (2018) reports that the phonetic cues for Hul'q’umi'num’ ejective exhibit a fair amount of variability. If this is interpreted as a lack of phonetic robustness, the prediction is that learners may have difficulty distinguishing ejective consonants from their plain counterparts, something we return to in Section 4.

Lexical familiarity/frequency is also a predictor of L2 phoneme intelligibility, as evidenced by the relationship between the two in ESL learners’ production of English vowels (Thomson and Isaacs, 2009). Using a three-level scale of familiarity (most familiar, second most familiar, least familiar), Thomson and Isaacs show that vowels are significantly more intelligible in the most familiar lexical context versus the least familiar lexical context and that vowels in the second most familiar lexical context are significantly more intelligible than vowels in the least familiar lexical context. They find that the intelligibility of six out of ten English vowels decreases as lexical familiarity decreases. Interestingly, the same pattern is found for the remaining four once the data is controlled for learners’ L1 backgrounds. Similarly, in a study of 217 native Spanish and Portuguese learners of English, Koirala (2015) reports a negative correlation between word frequency and the perception of word difficulty/learnability, finding that as word frequency increases, perceived difficulty decreases. She states that this relationship is similar to the one found also for L1; indeed, Grosjean (1980) and Marslen-Wilson (1987) have both shown that adult learners process familiar words faster than unfamiliar ones. Along the same lines, White at al. (2009) showed that listeners are more aware of phonetic detail in more familiar words. The latter finding might be especially valuable to keep in mind for Hul’q’umi’num’, in which awareness of phonetic details is particularly important, given how dense the consonantal space is (see Table 1).

Linked to word familiarity is the idea that the frequency of a particular sound or sequence affects its learnability. Sounds are often more easily acquired by a speaker in their L2 if those same sounds are found in the same phonetic environments in the speaker’s L1 (Flege, 1995). Perhaps particularly relevant for Hul’q’umi’num’ with its complex syllable structure, Morrison and Hudson Kam (2009) find that, among adult L2 learners, there is a negative correlation between how accurately a word is pronounced and the number of complex clusters found within that word. The assumption underlying this finding is that these complex clusters are unfamiliar to the learners from their L1. Along the same lines, Johnson et al. (2018) found that novel sounds tend to impede word learning, and this is especially true when words contain more than one unfamiliar sound. Because of the richness of the phonological and morphological systems in Hul’q’umi’num’, many words contain multiple unfamiliar sounds and sequences. This factor is one that we will explore in Section 4.

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Archibald (2005) delves even deeper than the segmental level, arguing that active phonological features in L1 can be redeployed by L2 speakers, potentially increasing the success of L2 pronunciation. For instance, Atkey (2001) posits that English learners of Czech are able to acquire palatal stops, because Czech palatal stops and English post-alveolar fricatives both make use of the phonological feature [palatal]. At least some of the Hul’q’umi’num’ sounds that are not shared with English do include featural content that is shared, e.g. the features of Hul’q’umi’num’ <tth> are found, albeit separately, in English <t> and <th>. It is conceivable that Hul’q’umi’num’ learners are able to draw on the building blocks of English sounds to construct the more complex sounds of Hul’q’umi’num’.

Finally, it is worth mentioning that effects related to learner age (Herschensohn, 2000; Lenneberg, 1967; Bley-Vroman, 1990), language experience/exposure (Flege et al., 1997; Bohn and Flege, 1992; Trofimovich and Baker, 2006), and training (Pisoni et al., 1982; McClaskey et al., 1983; Jamieson and Morosan, 1986, 1989; Wang et al., 2003) have also been documented, although findings are mixed. In order to protect the anonymity and confidentiality of the learners of our study, we do not compare them based on their prior experiences with the language. We do note, though, that thirteen of the thirty-five learners reported exposure to Hul’q’umi’num’ between the ages of one and six, and approximately half had some experience with the language before enrolling in the HLA classes. Bashan and Farthman (2008) argue that latent speakers, those having grown up hearing some amount of the language, are at an advantage in learning the sound system, compared to those who have no prior experience with it. We expect that, in general, the pronunciation of the learners in our study will be better than we might expect of learners with no prior exposure to Hul’q’umi’num’.

In summary, while L1 transfer and universal markedness effects clearly create challenges for L1 Hul’q’umi’num’ learners, the social and pedagogical contexts in which they are learning the language may help them to overcome these challenges, more so than in more typical language learning contexts. In addition, word-level, phoneme-level, and even feature-level properties may affect the ease with which Hul’q’umi’num’ produce the sounds of the language. In short, there is much to explore when it comes to Hul’q’umi’num’ L2 pronunciation.

2.3 Expectations

Our study provides the first broad scan of the pronunciation characteristics of L2 learners of a Salish language. Given its exploratory nature and the potential interaction of many factors influencing L2 Hul’q’umi’num’ pronunciation, it is difficult to make concrete predictions about what we will find. Based on previous pilot studies and one the literature review presented in 2.2, we predict that we will see errors that converge on English (due to L1 transfer and markedness), but also ones that diverge from English (due to hyper-articulation of distinctive Hul’q’umi’num’ sounds); we expect that errors involving glottal gestures will be particularly common, as will ones involving coronal obstruents, especially in clusters; finally, we expect that familiarity at the word, the segment, and the featural level will also affect error rates. These effects and others are explored in reference to observed error patterns presented in Section 4 below.


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3 Methods

This study is based on a set of four pronunciation tests given to different groups of Hul’q’umi’num’ learners between 2016-2019 during language classes hosted by the Hul’q’umi’num’ Language and Culture Society (HLCS) in Duncan, BC, and taught through the Hul’q’umi’num’ Language Academy (HLA), which offers undergraduate and graduate level diploma and degrees programs in Hul’q’umi’num’ Linguistics in partnership with Simon Fraser University. The dataset is relatively large, but not highly controlled, because it is made up of separate pronunciation tests that were designed for immediate pedagogical purposes rather for a single, broad research project. Therefore, the tests differ in the words they targeted and in the elicitation method they used (see Section 3.2). Nonetheless, the findings (Section 4) contribute to increasing our understanding of L2 pronunciation acquisition in languages that are very different from those typically studied in the field, and provide a solid foundation for future, more controlled work on L2 pronunciation in the Hul’q’umi’num’ context.

3.1 Participants

In total, 35 learners participated in the pronunciation tests. All reported English as their L1. All but one were Hul’q’umi’num’, but they varied widely in their ages and in their previous experiences with the language.2 The youngest participant was in their mid-20s; the oldest was in their late 70s. The majority of the participants had some kind of exposure to Hul’q’umi’num’ as a child, in a variety of local dialects from across Hul’q’umi’num’ territory. Some older participants report using the language as a very young child and then not using it again for a number of years. A few students had no exposure to and did not use Hul’q’umi’num’ in childhood. These participants reported learning Hul’q’umi’num’ after the age of 20. In terms of formal language education, some had had Hul’q’umi’num’ lessons in childhood, whereas others had not. All of the participants had some degree of university-level experience with Hul’q’umi’num’.

3.2 Pronunciation tests

Table 2 summarizes the pronunciation tests that the data analysis is based on. These all consisted of word lists, elicited in isolation; for the complete word lists, see Appendix B. Generally speaking, the words were relatively frequent, and learners would be expected to come across them in their day to day language use. The shortest test had ten words (the Numbers Test); the longest had 48 words (Prontest 1). The 2016 Prontests 1 and 2 focused on coronal obstruents, in particular in initial clusters, which are very common in Hul’q’umi’num’. The Numbers test and the 2018 Prontest focused on semantic content rather than phonological content. The Numbers test included Hul’q’umi’num’ numbers 1-10, which contain many challenging sounds and clusters in them. The 2018 Prontest focused on animal names.

All of the tests were administered at least once. Three of the tests were administered a second time in 2019, following the same general procedure, and with a new group of speakers (learners). A small number of speakers who continued in the Hul’q’umi’num’ language program between 2016 and 2019 repeated one or more of the tests, e.g. they took Prontest 1 in 2016 and again in 2019. For this study, so as not to skew the data towards any particular students, we only included data from the first test, for students who took it more than once. In Table 2, the number of words corresponds to the total number of words in each test. The number of speakers corresponds to the total number of speakers who took the test, including both the original and the 2019 versions, if relevant. The number

2 We do not share detailed information on individual learners, because they form a very small group, and this would betray their confidentiality.


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of tokens analyzed is not a simple multiplication of words by speakers: it includes repetitions, in cases where speakers repeated a given word more than once (because they sometimes made different errors across repetitions), and it excludes tokens that were discarded for some reason, e.g., because background noise made it impossible to hear the word.

Table 2. Pronunciation Tests that formed the basis for this study.

Test name Focus/content # words # speakers # tokens analyzed

Prontest 1

(2016 + 2019)

Coronals - Set 1 (and initial clusters)

Imitation task

48 17

974

Prontest 2

(2016 + 2019)

Coronals - Set 2 (and initial clusters)

Reading + imitation task

24 17 644

Prontest 2018

(2018 + 2019)

Animals

Imitation task

30 20 1176

Numbers

(2016)

Numbers

Memory task

10 12 121

TOTAL # words 112 35 2915

All pronunciation test sessions were recorded using Audacity (48 KHz and 16-bit uncompressed .wav files) and a Yeti USB microphone in cardioid mode connected to an Apple iMac computer. Generally speaking, the tests were all carried out in small groups. Each group consisted of an Elder, two or three students, and a linguist. The students took the test one at a time. While one student was taking the test the other student(s) was/were working the microphone and Audacity under the guidance of the linguist, as well as ensuring that the sound file was successfully saved, again under the guidance of the linguist. The Elder’s role varied, as outlined below.

Prontest 1 (2016 and 2019) was a direct imitation task (henceforth simply called "imitation" task) (Jilka et al. 2007), in which the Elder read one word at a time off a list and the learner repeated after them. In the 2016 test, each word was pronounced a single time by each speaker: Elder, then learner. In 2019, each word was pronounced twice, in the following sequence: Elder - first repetition; learner - first repetition; Elder - second repetition; learner - second repetition. Prontest 2018 was also

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an imitation task, in which each word was repeated twice (or sometimes more), in the same sequence as the 2019 Prontest 1. Prontest 2 (2016 and 2019) was a mixed reading-imitation task. The learner and the Elder both had access to the same reading list. The student read one word at a time from the list (reading task); then the Elder said the word and the student repeated it (imitation task). In these first three tests, the Elders were instructed not to provide any corrective feedback to the learner during the tests, although sometimes they did anyway. The Numbers test was a spontaneous speech task, in which learners were asked to recite the number 1-10 from memory. Jilka et al. (2007) provide a discussion of different tasks and the skills and processing demands they tap into, which we return to in Section 4.1.1.

Given the range of tasks and procedures used, it is possible that some of the errors observed were specific to certain tests and tasks. Since the goal of this study was to gain a broad understanding of the types of errors made by Hul’q’umi’num’ learners, we did not conduct a detailed analysis of the data by test (though see Section 4.1.1 for a broad comparison of tests). It would certainly be worth following up with more controlled elicitations to test for task effects. We note here that one learner in particular commented to us that they felt their pronunciation was different in different tasks.

3.3 Data coding and analysis

The pronunciation tests yielded a total of 2915 elicited words (see Table 2). Data coding was done auditorily in Praat (Boersma and Weenink, 2020), with reference to the visual displays (primarily the spectrogram) as well. Errors were coded by co-author Leonard using the Textgrid function, by listening for differences between the learner’s pronunciation and that of the elder, and in reference to the orthographic form. Both the specific error (e.g., <th> → <lh>) and the whole word (e.g. thqet → lhqet ‘tree’) were transcribed, in separate Textgrid tiers. Co-author Nolan re-coded 30% of the recordings (randomly selected). The agreement rate between the two was 83%3. It is important to note here that our data coding process involved certain limitations, which must be taken into consideration in interpreting the findings presented below. In particular, we transcribed the data ourselves, as Coast Salish sound experts but not as fluent Hul’q’umi’num’ speakers. Although in the field of phonetics, it is not unusual for non-speakers to document speech, this is more unusual in the field of SLA. There is simply not the capacity right now in the Hul’q’umi’num’ speaking community to have L1 speakers code such a large set of data. While we are all experienced phoneticians, and have been working on Hul’q’umi’num’ and closely related languages for many years, our ears are not those of L1 speakers or of teachers. As a result, it is likely that we missed errors related to the more subtle contrasts, e.g., between velar and uvular stop consonants, and therefore that our dataset under-represents these types of errors. A compounding factor is that many words contained multiple errors, making it difficult to catch all of them. Interestingly, in our experience, L1 speakers who are engaged in language teaching are not always as thorough in noticing pronunciation errors as we (phoneticians) are because they are focused on communication and on giving learners only as much feedback as they feel they are ready for. It would be certainly interesting to conduct a smaller-scale study comparing errors coded by phoneticians vs. L1 speakers. In any case, although we are confident that the general patterns summarized in Section 4 below are representative of L2 speakers’ pronunciation, we caution the reader against taking the precise numbers provided (e.g., error ratios of different types) too literally.

Orthographic form, transcribed form, and specific error were automatically extracted via a Praat script into a spreadsheet, as well as information on the learner and pronunciation test.

3 Most errors were related to glottalization, and whether or not it was heard as present.

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Information on a number of other properties of each error was then added to the spreadsheet, including word position, syllable position, stress position, and whether or not the target sound occurred in a cluster (for consonants). Errors were also coded for general type (e.g., voicing, place, manner) as well as for more specific types of error (e.g., within manner: fricative → stop). Table 3 provides examples of partial entries coded for some of these fields.

Table 3. Examples of data coding.4

Orthog. Transc. Error Syllable position

Stress position

Error-general

Error-details

L1 relation

thqet lhqet th → lh #_CV pre-stress place dental → lateral

diverge

shes shelh s → lh VC# post-stress place lateral → alveolar

diverge

kwushou kushou kw → k #_V unstressed labial de-labial converge

spaal’ spaal l’ → l V_# post-stress glottal de-glottal converge

We also coded for two other features that we thought might be useful in analyzing the patterns discovered. First, we coded for the number of unfamiliar sounds and sound sequences (separately) in the word, as a rough measure of complexity of a word’s sound structure5. For example, the word ‘apun (‘ten’) had no unfamiliar sounds or sequences, so received a score of 0. The word t’xum (‘six’) has two unfamiliar sounds (<t’> and <x>) in addition to an unfamiliar sequence (<t’x>) so it received a score of 3. Second, we coded whether the error seemed to be converging on English (e.g. <lh> → <th>), diverging from English (e.g. <th> → <lh>), neither clearly diverging from nor converging on English (e.g. <xw> → <hw>), or converging on a less marked but non-English form (e.g. <sts> → <ts> word initially). Because the Hul’q’umi’num’ sound system is so much more complex than the English one, convergence on English also generally involved convergence on a less marked form. Cases where L1 transfer and markedness were impossible to disentangle from one another (the majority of cases) were coded as converging on L1.

During the process of adding information to the spreadsheet, each data point (row in the spreadsheet) was checked for consistency, in particular to make sure that the error coded was consistent with the orthographic form and the transcribed form. Cases where inconsistencies were found (e.g. where an error was marked, but the transcription matched the orthographic form) were

4 In this table and following ones, please refer to Appendix B for translations of the example words provided.

5 Since the Hul’q’umi’num’ sound structure is more complex than the English one, we assumed that unfamiliar sounds and sequences were also more complex ones.


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flagged, and were checked in the raw audio file. This process served as an additional reliability check on the data.

4 Results

Of the 2915 tokens analyzed, 1336 had at least one error in them. Many tokens had more than one error, such that, in total, 2043 errors were coded and analyzed. Across tests, 259 specific types of errors were coded, categorized into 19 general types of errors, the most common relating to voicing (Table 7). The following sections present the results in a number of ways: 4.1 summarizes the overall results by test (4.1.1), by learner (4.1.2), and by word (4.1.3). 4.2 explores the errors in more detail, considering error rates by voicing, manner, and place of articulation (4.2.1), by combination of phonological properties (4.2.2), and by whether errors converged on or diverged from English (4.2.3). Within each section, we focus on the results that are the most relevant to identifying error patterns. Because of the broad range of results presented, we provide a discussion of the results within each sub-section.

4.1 General results

4.1.1 Errors by tests

As mentioned above, the pronunciation tests were not designed to make them comparable to one another, in terms of the specific words elicited or the elicitation task (see Section 3). Nonetheless, it is worth taking a look at whether performance differed across tests and, if so, what factors might explain the observed differences. Table 4 summarizes error rates across tests. Column 1 provides the test that subset of data come from. Column 2 provides the raw number of errors per test (not directly comparable across tests because of different numbers of learners and elicited words). Columns 3 and 4 provide two different error ratios: Column 3 provides the number of words with errors, as a percentage of the number of words elicited. For example, Prontest 1 (2016 + 2019) included a total of 974 words elicited (across speakers); 381 of these had at least one error in them, meaning 39% of the words elicited in Prontest 1 had at least one error in them; Column 4 provides the average error rate per word, based on the total number of error6 and the total number of words with errors for each test. For example, Prontest 1 (2016 + 2019) had a total of 381 words with errors in them, and a total of 547 errors within those words, leading to an average of 1.44 errors per word. Column 5 includes the mean number of unfamiliar sounds and sequences per word, as a reference for explaining error rates.

6 Unless otherwise specified (Section 4.1.2 and 4.1.3), numbers of errors correspond to error tokens rather than types.


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Table 4. Errors by test.

Test # errors % # words with errors/# words

Ratio # errors/#words with errors

Mean (SD) # unfamiliar sounds and sequences

Prontest 1 2016 + 2019

(imitation)

547 39% 1.44 1.92 (1.29)

Prontest 2 2016 + 2019

(reading + imitation)

663 60% 1.71 3.71 (1.21)

Prontest 2018 + 2019

(imitation)

773 45% 1.47 1.82 (1.25)

Numbers 2016

(lexical retrieval)

55 33% 1.38 1.9 (1)

Overall, the test that learners performed the best on, according to error percentage (33%) and ratio per word (1.38), was the Numbers test. The mean number of unfamiliar sounds and sequences per word was 1.9 in the Numbers test, very similar to Prontest 1 and slightly higher than Prontest 2018, so phonological complexity is not likely at play. Instead, better performance of learners on the Numbers test is likely due to the frequency and familiarity of the words involved (Thomson & Isaacs, 2009; Koirala, 2015): numbers are one of the first things to be taught in any language, and Hul’q’umi’num’ is no exception, especially because many adult learners are expected to teach in the schools very soon after they themselves start learning. The learners would have had a lot of practice with these words, and likely be relatively comfortable pronouncing them, as reflected in their ability to recall them from memory. The fact that errors rates were lower on the Numbers test than on Prontest 1 (2016 and 2019) and Prontest 2018 also suggests that the direct imitation task used in Prontest 1 and Prontest 2018 did not lead to a temporary pronunciation accuracy that exceeded the actual competence of learners, as suggested by Jilka et al. (2007).

Prontest 2 showed by far the highest error percentage (60%) and average error rate (1.7). This was a reading task, focused on word-initial clusters of coronal consonants, e.g. lhts’iws (‘tired’) and shts’e’nutstun (‘chair’). The average number of unfamiliar sounds and sequences per word in Prontest 2 was 3.71, much higher than any other test. Given that the standard deviation is almost the same for all tests, the higher mean number of unfamiliar sounds and sequences in Prontest 2 is likely to reflect a real difference in the phonological complexity of the words in this test compared to the others. In addition, Prontest 2 had learners read words from a list, before imitating them after an


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Elder/teacher. Thomson and Isaacs (2009) found that vowels were produced more intelligibly when elicited via an auditory (imitation) task than via a reading task. While it is not possible, with our dataset, to tease apart effects of task and complexity, we note here that learners often struggle with reading and writing Hul’q’umi’num’, and much prefer imitation tasks to reading tasks (as expressed to us during various tests). This is in keeping with a general preference for and focus on oral language proficiency over literacy, which holds for Indigenous language learning and teaching in general7. Thus, we suspect that the combination of words with relatively complex sound structures and a reading task combined to make Prontest 2 (2016, 2019) a particularly challenging test, and leading to particularly high error rates.

4.1.2 Errors by word

Words varied substantially in their error ratios. These ratios can be calculated in two ways, using (a) the total number of error tokens, i.e. the total number of errors or (b) the total number of error types, i.e., the number of different errors. With (a), multiple instances of a single error are counted separately whereas with (b), multiple instances of a single error are counted only once. Using both types of computation allows us to gauge how much consistency there is in pronunciation errors within a given word. We expect that some words will be consistently mis-pronounced in the same way, leading to high token-based error ratios but low type-based error ratios; other words will show less consistency, showing more equal error ratios based on tokens vs. types. Table 5 provides the words with the lowest and highest error ratios, along with the mean and median error ratios, by error token and type. Error ratios were calculated as the total number of errors (by token or type) by the total number of tokens elicited, for each word.

7 The most popular methods for Indigenous language learning and teaching are immersion based and oral-based, and include methods like Total Physical Response (Asher 1977; Cantoni 1999) and Where Are Your Keys (https://whereareyourkeys.org).

https://whereareyourkeys.org/

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Table 5. Ranges of error ratios according to token and type, across words.

Error ratio (tokens) Error ratio (types)

Lowest ratio 0 (no errors)

Words: ‘apun, ‘ushul, lemut, mousmus, stem, toohw,

yuxwule’

0 (no errors)

Words: ‘apun, ‘ushul, lemut, mousmus, stem, toohw,

yuxwule’

Highest ratio 1.99

Word: kw'et'un'

0.67

Words: wetth'ut, shch'ekwxul's

Mean ratio 0.71 0.25

Median ratio 0.66 0.21

In terms of the lowest error ratios, it is interesting to note that toohw (‘nine’) and yuxwule’ (‘bald eagle’) contain no errors, despite the fact that they contain unfamiliar sounds (<hw>, <xw>, and final glottal stop). This again suggests that word frequency and familiarity play an important role in pronunciation: just as is the case with numbers, animals of the Pacific Northwest are among the first to be taught and learnt as well, so yuxwule’ is a word that is very familiar to learners.

Second, it is interesting to compare the words with the highest error ratios by token - kw’et’un’ (‘mouse’) - vs. by type sch’ekwxul’s (‘frying pan’) and wetth’ut (‘pry it’). The word kw’et’un’ is produced a total of 67 times in our dataset, and those productions include a total of 133 individual errors, leading to a ratio of 1.99 errors/word. While kw’et’un’ does not appear particularly complex at first glance, it has 3 sounds that involve a glottal gesture: ejective <kw’> and <t’> and glottalized <n’>. As we shall see below (Table7), errors involving glottal gestures (labelled voicing errors in 4.2) are by far the most common. Interestingly, the error ratio of kw’et’un’ by type is only 0.05, reflecting the fact that learners are very consistent in the types of errors that they are making: de-ejectivization and deglottalization. In contrast, the word wetth’ut only has a single unfamiliar sound in it (<tth’>), but it is mispronounced in six different ways: [tth], [t], [ts], [ts’], [th], and [ch], leading to a much higher error ratio by type, despite a middling (though above average) error ratio by token of 1. Bird et al. (2016) showed a similar pattern for <tth’>, based on a subset of the data under consideration here.

Abstracting away from individual words, error ratios are generally correlated with numbers of unfamiliar sounds/sequences. This is illustrated in Figure 2, which plots the number of unfamiliar sounds and sequences (x-axis) by the error ratio (y-axis), calculated on tokens (a) and types (b).

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(a) By token (b) By type

Figure 2. Error ratios by unfamiliarity of sounds and sequences, by token (a) and by type (b).

Figure 2 shows that words with no unfamiliar sounds/sequences (e.g., ‘apun ‘ten’) have low error ratios; words with four or more unfamiliar sounds/sequences (e.g. shch'ekwxul's ‘frying pan’) have high error ratios. Words with one to three unfamiliar sounds are the most variable in terms of error rate, likely as a function of word frequency (see above) and also individual learner (see Section 4.1.3). Spearman’s rho correlation coefficient showed a significant correlation between ratio of error tokens per word count and the number of unfamiliar sounds and sequences, rs = .66, p < .01, N = 106. Spearman’s rho for error types and unfamiliar sounds and sequences was also significant, rs = .62, p < .01, N = 106. The Findings in Figure 2 reflect patterns documented previously in relation to processing of unfamiliar sounds. For example, Johnson et al. (2018) found that as the number of unfamiliar sounds in a word increased (from zero to two), word learning tended to become more challenging (see also Grosjean, 1980; Koirala, 2015; Marslen-Wilson, 1987; Thomson and Isaacs, 2009; White at al., (2009).

4.1.3 Errors by learner

Table 6 has the same format as Table 5, but it is based on speakers rather than on words. It provides the lowest and highest error ratios (by tokens and types), as well as the mean and median error ratios.

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Table 6. Ranges of error ratios according to token and type, across speakers.

Error ratio (tokens) Error ratio (types)

Lowest error ratio 0 (no errors); 0.25 0 (no errors); 0.10

Highest error ratio 1.19 0.77

Mean error ratio 0.69 0.35

Median error ratio 0.74 0.33

To ensure confidentiality of learners (they are a small group, and individual speakers are easily identifiable), we do not report on specific individual differences that might explain the highest and lowest ratios by learner. Instead, we focus on the relationship between error ratios based on types vs. tokens. Discrepancies between these two ratios provides an indication of the consistency of the strategies used by learners to pronounce challenging sounds and sequences: learners with high error-token ratios but low error-type ratios are consistent in their strategies, e.g. consistently replacing <tth’> with <t’>. Learners with more equal error-token and error-type ratios are less consistent, e.g. replacing <tth’> with a range of sounds (see Section 4.1.2). Bird et al. 2016 found that, for <tth’> specifically, half the learners consistently used one sound when substituting for <tth’> while the other half made a variety of errors, and this did not correlate with degree fluency of the student. Here, two learners can illustrate this difference. One learner, of the inconsistent group, had 6 <tth’> errors, consisting of substitutions with <ch> (2), <st’> (1), <ts’> (2), and <tth> (1). Another learner, of the consistent group, had 20 errors with <tth’>, substituting with <ts> (11), <tth> (3), <s> (2), <’> (2), <ts’> (1), <th> (1). Even though the consistent learner had slightly more types of errors than the inconsistent learner (six vs. four), one particular type dominated: <ts> (11/20 tokens); for the inconsistent learner, the four types were equally distributed (1-2/6). Figure 3 shows the interaction of error tokens by speaker (y-axis), error types by speaker (x-axis), the number of words produced (size of data point), and ratio of error types to error tokens (colour of data point). A larger circle means more words produced while a lighter circle means a greater equality of token types and token errors.

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Figure 3. Error ratios by speaker; error ratio by type on the x-axis, error ratio by token on the y-axis. Each circle represents one speaker; the larger the circle, the more data for the speaker; the lighter the

circle, the close the by-type and by-token the ratios were.

Two patterns can be seen in Figure 3, indicated with the regression lines and split across a diagonal running from (0.1, 0.25) to (0.6, 1). This split corresponds to an error type / error token ratio of < 0.5 (the left group) and > 0.5 (the right group), 0.5 being the median error type / error token ratio. A ratio of 1 would mean that every single error is a unique type, the closest is the speaker at (0.72, 0.77), on the tail end of the right group, who has a type/token ratio of 0.93. The inverse would be a ratio of 0, meaning every error is of a single type. The closest are the points at (0.1, 0.37) and (0.32, 1.15) with type/token ratios of 0.27 and 0.28 respectively. On the right is the inconsistent group, who produce more error types per token, approaching a 1:1 ratio between types and tokens. On the left is the consistent group, who produce few error types but many error tokens. Importantly, the consistent group includes the speakers who produced the largest number of words in the tests. This suggests that, as speakers add tokens, they do not add variety in the repair strategies they use to pronounce challenging sounds.

4.1.4 Summary of general patterns

Summarizing general effects, it is clear that not all tests are equal in their level of difficulty - depending on the task (Jilka et al., 2007; Thomson and Isaacs, 2009) and on the phonological complexity of the specific words included (Morrison and Hudson Kam, 2009) - and not all speakers are equal in their pronunciation skills. It is no surprise that individual speakers vary in their pronunciation, as a result of prior experience with the language (see Basham & Fathman, 2008), physiological restrictions (e.g. hearing loss among older learners), and innate affinity for pronunciation work.

The finding that word familiarity (4.1.2) plays a role in pronunciation accuracy reflects the literature on frequency and familiarity effects (Thomson & Isaacs, 2009; Koirala, 2015). In fact, Hul’q’umi’num’ Elders who are also teachers have an implicit awareness of this effect and incorporate it into their pedagogical practices. When a learner has trouble with a particular sound, their Elders encourage them to come up with other, more familiar words that also contain the sound,

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and that the learner has no (or less) trouble with. They tell the learner to repeat the familiar words over and over before going back to the more challenging word. In this way, the Elders naturally incorporate familiarity into their teaching, in a way that is effective for the learners and that makes good sense given documented frequency and familiarity effects.

Finally, although beyond the scope of this paper, we hope to take a closer look in the future at error ratios based on token vs. type, to increase our understanding of the consistency with which challenging sounds and sequences are repaired, both within speakers and within words. We also hope to work with Elders to determine which types of errors they perceive as more/less problematic. Raising learners’ awareness of the variety of strategies used to repair challenging sounds facilitates their own pronunciation improvement as well as enables them to predict the types of repair strategies their own future students may use. Ultimately, this type of information has the potential to inform the content of pronunciation teaching materials, tailoring them in targeted ways that directly serve the needs of specific learners (e.g. participants in this study), and Hul’q’umi’num’ learners more generally.

4.2 Errors by phonological specification

Having looked at general error patterns across tests, words, and learners, we now turn to a more detailed look at errors as a function of the phonological properties of the target sounds and repair strategies used by learners. In all cases, counts are of error tokens (not types). Note that 223 out of the total 2043 errors were coded as mixed errors, meaning they included more than one error type. For example, <tth’> → <ts> includes both a place error (<th> → <s>) and a voicing error (ejective → plain). The error counts below include all errors of a particular type, even if they come from a mixed error. Practically, this means that total numbers of errors add up to 2299, which is more than 2043, since each of the 223 mixed errors include at least two individual errors.

Overall, nineteen general types of errors were coded, although some of these were very low-frequency. Table 7 summarizes the error types that accounted for at least 10% of the total errors: voicing, place, and manner substitutions, and deletion. Other errors included vowel quality (4% of total errors), labialization (3%), stress shift (1%), metathesis, vowel length, and syllable deletion (0.3% each), decomposition (e.g. <tl’> → [t’]+[l]) (0.1%), and a number of other marginal categories. By far the most common general error type was voicing (see also Table 8). This was no surprise; previous work also showed that errors in glottal gestures are very common among learners (Bird et al., 2016).


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Table 7. Number of errors by general phonological category.

Error type Most common error (#) Example

Number (% of total)

Voicing n’ → n (N=119)

kw'et'un' → kw’et’un

967 (42%)

Place lh → th (N=95)

snuhwulh → snuhwuth

466 (20%)

Manner ts → t (N=57)

stseelhtun → steelhtun

283 (12%)

Deletion ’ → ∅

stqeeye’ → stqeeye

254 (11%)

TOTAL - 4 most common error categories 1970 (85%)

Before considering errors involving sound substitutions (first three rows in Table 7), we first take a look at cases of deletion (last row in Table 7), focusing in particular on consonant deletion (vowel and syllable deletions were uncommon). Table 8 summarizes deletions by word and syllable position. Glottal stop is distinguished from other consonants, since glottal stop is the single most frequently deleted consonant, and it is deleted in positions that differ in systematic ways from other consonants.

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Table 8. Consonant deletion by position.

Word position Syllable position Most common error (#) Example

Number (% of total)

Glottal stop deletion

Final V_#, V_C# V_# (N=42)

stqeeye’ → stqeeye

53 (21%)

Medial V_V, V_CV V_V (N=30)

t’i’wi’ulh → t’i’wiulh

32 (13%)

Initial s_V s_V (N=1)

s’itth’um → sitthum

1 (<1%)

Other deletion

Final V_#, V_C#, VC_# V_#, l’ → ∅ (N=6)

spaal’ → spaa

23 (9%)

Medial V_CV, VC_V, C_C, C_V, (V_V)

V_CV, t → ∅ (N=19)

tsiitmuhw → tsiimuhw

66 (26%)

Initial #_CV, #C_V, #C_CV, #CC_V, #_CCV, #_V,

#_CV, s → ∅ (N=25)

stseelhtun → tseelhtun

77 (30%)

TOTAL 254

With very few exceptions, glottal deletions occur word-finally following a vowel (e.g. at the end of stqeeye’ (‘wolf’) or word-medially between two vowels, e.g. between and in t’i’wi’ulh (‘pray’). Glottal stop deletion also occurs in ten instances of swuqw’a’lh (‘mountain goat/wool blanket’), preceding final <lh>. We know from working with learners that they often leave off word-final glottal stop, and this finding reaffirms the importance of focusing learners’ attention to this particular error. In terms of word-medial intervocalic position, our impression is that, even for L1 speakers, the glottal stop is not always phonetically robust (c.f. Archibald, 2009). This is an area that warrants further study, based on acoustic analysis; if learners are not hearing glottalization from their Elders (their L1 models), then it is not surprising that they are not producing it.

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Other consonants are more varied in their specific deletion sites, but with very few exceptions, these sites are ones involving sequences of consonants, either word-initial or word-final tautosyllabic clusters or word-medial heterosyllabic sequences. The most common site for deletion is in word-initial clusters. This is likely partly a result of the particular dataset we are working with, which includes two tests (Prontest 1 and Prontest 2) that specifically focused on coronal clusters, many of which are word-initial. Nonetheless, they reflect previous findings on the difficulty of clusters for language learners (e.g. Davidson, 2006; Morrison & Hudson Kam, 2009), and they point to the particular challenges that Hul’q’umi’num’ learners face with words like ts'lhhwulmuhw (‘fellow First Nations person’).

In the following sections, we turn to sound substitutions, taking a closer look at the general patterns outlined in Table 7. In Section 4.2.1 we consider errors by voicing, place, and manner; in Section 4.2.2, we explore patterns of co-occurrence across phonological properties among errors coded as ‘mixed’; in Section 4.2.3, we break down errors based on whether they converge onto English (as an effect of L1 transfer and/or markedness), diverge from English (as an effect of over-emphasizing distinctively Hul’q’umi’num’ sounds), or something else.

4.2.1 Errors by voicing, manner and place

Tables 9-11 break down the results of Table 7 further, examining error types by voicing, place, and manner of articulation. Table 9 considers voicing, which encompassed errors relating to glottal gestures: glottalization (e.g. <l> → <l’>), deglottalization (e.g. <l’> → <l>), ejectivization (<t> → <t’>), and de-ejectivization <t’> → <t>). Column 1 provides the general sound category, for reference; the specific type of error is provided in Column 2; the most common sound substitution and an example is provided in Column 3; Column 4 provides the raw number of errors of that type, along with its percentage, based on the total number of voicing errors in the dataset.

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Table 9. Number of errors by voicing.


Number (% of total)

Ejectives

Ejective to plain t’ → t (N=106)

kw'et'un' → kw’etun’

528 (55%)

Plain to ejective ts → ts’ (N=17)

tsiitmuhw → ts’iitmuhw

47 (5%)

Glottalized resonants

Glottalized to plain n’ → n (N=119)

kw'et'un' → kw’etun

387 (40%)

Plain to glottalized

y → y’ (N=1)

tum'xuytl' → tumlhuy'

1 (<1%)

Other Voicing/devoicing ts → dz (N=3)

tsakw → dzak

4 (<1%)

TOTAL 967

The most common voicing errors were de-ejectivization of ejective stops and affricates (55% of voicing errors; 26% of all errors) and deglottalization of glottalized resonants (40% of voicing errors; 19% of all errors). Ejectives and glottalized resonants do not occur in English. In addition, their phonetic robustness is variable, even among L1 speakers (Archibald, 2009; Percival, 2018). Thus, this pattern was expected. One particular sound replacement worth mentioning is <tth’> → <tth>, which occurred 45 times. This is interesting because <tth> is in fact extremely limited in its distribution, occurring only in a small set of function words. Learners are taught that <tth> does not occur outside of this set, and yet they do use it repair the more challenging <tth’>. Possibly, this is done to stick with a sound that is clearly Hul’q’umi’num’, but less marked than the target <tth’>.

Unlike what Gonzales (2008) found for learners of Yucatec Maya, word position does not have a considerable effect on de-ejectivization among Hul’q’umi’num’ learners. Across word lists (see Appendix B), ejectives are distributed across word positions as such: 56% word-initially, 27% word-medially, and 17% word-finally. De-ejectivization ratios mirror this distribution almost exactly (52%, 31%, and 16%, respectively), meaning that de-ejectivization ratios are exactly as we would

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expect given the distribution of ejectives in the word lists. A Chi-square test of independence finds the difference not statistically significant (X2 (5, N = 1510) = 2.74, p = .25). Across word lists, glottalized resonants are distributed 56% word-medially and 44% word-finally (they do not occur word-initially in Hul’q’umi’num’). Ratios of deglottalization are 63% and 37% respectively, slightly more than one would expect word-medially, and slightly less than one would expect word-finally, but the difference does not reach statistical significance (X2 (3, N = 948) = 2.90, p = .09).

For glottalized resonants, deglottalization is slightly affected by the timing of glottalization (pre- vs. post-glottalized). In Hul’q’umi’num’, glottal timing is determined by word-level stress8. Generally speaking, glottalized resonants are post-glottalized, except when they immediately follow a stressed vowel. For example, in sq'i'lu (‘preserved food’), <’l> is pre-glottalized following the stressed full vowel and preceding the unstressed (schwa); in squl’ew (‘beaver’) <l’> is post-glottalized in the opposite environment, following the unstressed and preceding the stressed full vowel <e>. Post-glottalized resonants (e.g. in squl’ew) are slightly more likely to deglottalize than are pre-glottalized resonants (e.g. in sq’i’lu): post-glottalized resonants make up 70% of glottalized resonants in the word lists (Appendix B), but 86% of deglottalized resonants. Conversely, pre-glottalized resonants make up 30% of glottalized resonants in the word lists but only 14% of deglottalized resonants. This difference is statistically significant (X2 (3, N = 952) = 14.4, p < .01). This pattern requires further attention; it is possibly a reflection of the degree to which the glottal gesture interrupts the flow of the syllable towards the stressed vowel: in squl’ew, the glottal gesture interrupts the flow between the onset consonant (<l’>) and the stressed <e>, whereas in sq’i’lu, the glottal gesture does not, since it occurs after the stressed . Possibly this interruption is particularly prone to deletion, for reasons related to syllable well-formedness (Clements, 1990; Gussenhoven & Jacobs, 2011).

Although relatively uncommon, there were also a number of errors that involved pronouncing a plain stop or affricate as an ejective (47 total; 5% of voicing errors). Ejectivization was particularly common word-initially (39; 83% of ejectivization errors), and most often involved <ts> → <ts’> (17; 36% of ejectivization errors)9. This result confirmed Bird et al.’s (2016) findings (based on a subset of the current data), where all but the most fluent learners replaced word-initial <ts> with <ts’> in at least one word, and one intermediate student did this for all word-initial <ts> tokens. This result is compatible with previous work that has shown that ejectives are more reliably pronounced in onset than in coda position (Gonzales 2008), pointing to the inherent compatibility of ejectives and initial position. It is also likely a manifestation of over-emphasis of sounds that distinguish Hul’q’umi’num’ from English, as a marker of social identity and also as a reflection of the pedagogical context in which the pronunciation tests were conducted and, more generally, in which language use occurs. A number of researchers have talked about glottalization being over-used as a marker of social identity (Babel, 2009; Haynes, 2010; Bird, 2020). As Haynes notes “it is likely that the sounds are used to create a perceptual distance from English, and possibly to index speakers’ identities as Native American” (p. 112). In terms of pedagogical context, Bird (2020) draws on Poeteren (2012) and Uther, Knoll and Burhnam (2006), suggesting that in SENĆOŦEN, a neighbouring Coast Salish language to Hul’q’umi’num’, ejectives are becoming more prominent (phonetically speaking) partly

8 In general, a word has a single full vowel (most commonly , <e>, or <a>), which is the stressed vowel; other vowels are schwa, written as .

9 Stress did not seem to have a strong effect on ejectivization: of the 39 word-initial ejectivized tokens, only 20 were pre-stress.


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as a consequence of teachers and learners wanting to increase the salience of these non-English sounds.

Table 10 is structured similarly to Table 8, but focuses on place of articulation.

Table 10. Number of errors by place.


Number (% of total)

Lateral → Dental lh → th (N=95)

snuhwulh → snuhwuth

101 (22%)

Dental → Alveolar tth’ → ts (N=31)

p'utth'tun → p'uts'tun

84 (18%)

Uvular → Velar q’ → k (N=33)

qiq'quq'ul's → kikkukuls

60 (13%)

Dental → Lateral th → lh (N=43)

thqet → lhqet

59 (13%)

Lateral → Alveolar tl’ → t (N=16)

wutl'uts' → wututs

37 (8%)

Alveolar → Palatal s → sh (N=11)

sum'shathut → shumshathut

16 (3%)

Palatal → Alveolar sh → s (N=13)

shts'e'nutstun → sts'e'nutsun

16 (3%)

Alveolar → Dental ts → tth (N=8) 14 (3%)

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stseelhtun → sttheetun

Velar → Uvular hw → xw (N=8)

stsuhwum→ stsuxwum

14 (3%)

Lateral → Uvular lh → x (N=9)

snuhwulh → snuhwux

10 (2%)

Other 55 (12%)

TOTAL 466

As we saw with ejectivization and de-ejectivization, we also see bi-directional place substitutions, specifically <th> → <lh> ~ <lh> → <th> and <s> → <sh> ~ <sh> → <s>. It is likely that the precise direction of change is determined by a number of factors, including ones that we are unable to explore here, e.g. analogy to (high-frequency) similar-sounding words. In terms of <th> → <lh> ~ <lh> → <th>, L1 transfer effects predict <lh> → <th>, but we also see a substantial number of <th> → <lh> substitutions, which may be due to overemphasis of non-English sounds, as argued for <ts> → <ts’>. Interestingly, there are also a small number of cases where <lh> is pronounced as <x>, possibly indicating that learners know they are aiming for an unfamiliar fricative, and do not quite hit the right one.

In terms of <s> → <sh> ~ <sh> → <s>, we suspect that, in some cases, the direction of the change relates to analogy based on morphological structure. In particular, of the 13 <sh> → <s> changes, all but two occur word-initially in consonant clusters, e.g., shts'e'nutstun → sts'e'nutsun (‘chair’). We hypothesize that this particular replacement may be due to learners mistakenly analyzing the initial coronal fricative as the s- prefix, which is probably the most common prefix and has several different grammatical functions.

Table 10 also includes a number of mixed errors, in which a change in place of articulation is accompanied by a change in voicing, specifically de-ejectivization (e.g., tth’ → ts). These seem to reflect a more holistic kind of simplification, and are discussed in detail in Section 4.1.2.

Finally, Table 11 summarizes errors by manner of articulation.

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Table 11. Number of errors by manner.


Number (% of total)

Affricate → Stop ts → t (N=57)

stseelhtun → steelhtun

124 (44%)

Affricate → Fricative ts → s (N=15)

tsiitmuhw → siitmuhw

55 (19%)

Fricative → Stop hw → kw (N=11)

’es-hw → ’eskw

30 (11%)

Fricative → Affricate s → ts (N=5)

slhelhuq’ → tslhelhuq’

22 (8%)

Stop → Fricative q → x (N=3)

sququweth → squxuwets

17 (6%)

Fricative → Resonant lh → l (N=9)

slhewun → slewun

11 (4%)

Other 24 (8%)

TOTAL 283

The most common errors involve simplification of <ts>, either to the stop <t> (44% of manner errors) or the fricative <s> (19% of manner errors). Almost all of these simplifications occur word-initially; <ts> → <s> generally occurs when the initial <ts> is immediately followed by a vowel (e.g., tsiitmuhw ‘horned owl’) whereas <ts> → <t> generally occurs in initial sts clusters (e.g. stseelhtun ‘salmon’). The latter sound replacement is interesting in that it happens in very high-frequency words like ‘salmon’. Despite what we said earlier, lexical frequency clearly does not

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determine all patterns of error. The opposite sound change, <s> → <ts> only occurs in initial clusters, when <s> is followed by another consonant (e.g., slhelhuq ‘lying down’). A more thorough investigation of initial coronal clusters is warranted, to determine whether learners are using <s> and <ts> in systematic (allophonic) ways word-initially, and if so, what precisely determines their distribution.

Another relatively common sound substitution involves strengthening fricatives to stops, especially in velar and uvular places of articulation (27/30 cases), e.g., in es-hw (‘seal’). Fortition of this kind is expected based on L1 transfer, since English has ‘back of the mouth’ (velar) stops but not fricatives. This pattern is interesting because L1 English learners of other languages with velar/uvular fricatives, such as German, often assimilate these fricatives to [h], not [k] (Scott, 2019). It is possible that the labialization of <hw> prompts replacement to <kw> because English lacks a labialized glottal fricative, but does have allophonic labialized velar stops in word-initial position, e.g. in coot, and also has [kw] sequences, e.g. in queen (see Archibald (2005) on redeployment). Phonetically, fricative strengthening is also compatible with articulatory ease, since stops require more fine-tuned articulatory mechanisms than do fricatives (Gick, Wilson, & Derrick, 2013).

Finally, there were a number of <lh> → <l> replacements. Six of these were word-initially, preceded by <s>, e.g., in slhewun (‘bulrush mat’). The remaining replacements were medially, in slhelhuq' (‘lying down’), possibly as a result of dissimilation.

4.2.2 Mixed errors

As mentioned above, many errors (N=223) involved more than a single phonological property. To get a sense of whether certain feature substitutions tended to co-occur, we analyzed the mixed errors as a separate set. No one error makes up a majority of mixed errors. In general though, the majority involve coronal consonants (including the four most common errors - see Table 12) and velar/uvular consonants. Of 78 different types of errors, 50 involved a coronal consonant, 24 involved a velar or uvular consonant, 2 involved both, and 2 involved other sounds.

Table 12. (Most common) mixed errors.

Error Example Number (% of total)

tth’ → ts wetth’ut → wetsut 32 (14%)

tl’ → t tum’xuytl’ → tumxuyt 16 (7%)

tth’ → t tth'ihwum → tihwum 15 (7%)

ts’ → s ts’umiil’ → sumiil 12 (5%)

As Table 12 shows, the coronal consonant mixed errors are dominated by de-ejectivization of affricates. The most common type of coronal mixed error involves <tth’>, with 63 errors. Forty-seven of these are <tth’> → <ts> (N=32) or <t> (N=15) (see Table 11). These mixed errors

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outnumber the place errors<tth’> → <ts’> (N=19) or <t’> (N=12). This preference for de-ejectivization is also apparent for the 30 mixed errors involving <tl’>; 21 are de-ejectivized, <tl’> → <t> (N=16), <ts> (N=3), <tth> (N=2), compared to the 5 errors where ejectivization is maintained <tl’> → <t’> (N=2), <ts’> (N=2), <tth’> (N=1). This pattern repeats with 24 <ts’> errors, 12 to <t> and 12 to <s>, compared to only 1 to <t’>.

Mixed velar/uvular errors included 26 uvular → velar and 7 velar → uvular errors. Table 9, above, showing only place errors, records 60 uvular → velar place errors and 14 velar → uvular place errors overall, meaning that 43% of uvular → velar and 50% of velar → uvular errors were mixed, also involving another error type (manner, labialization, or ejectivization). Of the uvular → velar mixed errors 13 involved de-ejectivization, that is, q’/qw’ → k/kw. In fact, there are no uvular → velar errors involving <q’> or <qw’> where ejectivization is maintained. This matches what Bird (2015) found in her work on SENĆOŦEN L1 and L2 pronunciation, where the uvular/velar split was also an ejective/plain split. When there was a velar → uvular error, in only one case was it a fricative → stop error as well (<hw> → <qw>); the other velar → uvular mixed errors involved stop → fricative or delabialization.

What the mixed errors show is a holistic simplification. For both coronal consonants and velar/uvular consonants, change of place is accompanied by change of voicing more often than change of place alone. Not only do place changes often converge on English (L1) sounds (see Section 4.1.3) - Hul’q’umi’num’ dental, lateral, and uvular sounds becoming L1-like alveolar and velar sounds, but the mixed errors go a step further by simplifying voicing as well.

4.2.3 Convergence and divergence from English

In Section 2, we introduced the idea of conflicting forces on Hul’q’umi’num’ L2 pronunciation: L1 transfer effects on the one hand and emphasis of distinctly Hul’q’umi’num’ sounds on the other. To explore how these might be playing out in the dataset at hand, we set out to code errors according to whether they converged on or diverged from English. In the end, we used five distinct codes (Table 13): (1) Hul’q’umi’num’ → English (convergence) was used for errors that seemed to show L1 transfer effects, including cluster simplification and sound deletion, de-ejectivization and deglottalization, fronting (uvular → velar), and strengthening (velars fricatives → stops). Because the English sound system is simpler than the Hul’q’umi’num’ one, convergence on English most often also corresponded to a decrease in markedness. (2) English → Hul’q’umi’num’ (divergence) was used for errors that resulted in forms that sounded more distinctly Hul’q’umi’num’ than the target words, including what we called cluster ‘complexification’, ejectivization, and glottalization. (3) Hul’q’umi’num’ → Hul’q’umi’num’ errors were ones that resulted in another sound that was not English, and where markedness effects were also not clearly at play; these included de-ejectivization of uvular stops and coronal affricates, delabialization, labialization, and ejectivization of uvular sounds. (4) Hul’q’umi’num’ → unmarked errors were ones that involved a decrease in markedness without resulting in an English sound. By and large these involved cluster simplification in initial onset position, where the result was a single onset consonant not found in English. Finally, (5) Unclear errors were ones that did not clearly fall into one of the other four categories, including different kinds of sound substitutions (e.g., vowel quality changes) as well as deletions and insertions that were not clearly motivated by markedness considerations or by a particular relationship with English. These required further analysis. Table 13 summarizes these five categories of errors.


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Table 13. Number of errors by direction of error.

Error type Example Number (% of total)

Hul’q’umi’num’ → English

Convergence on English

(L1 transfer; markedness)

qw’ → kw

smuqw’a’ → smukwa’

1195 (59%)

English → Hul’q’umi’num’

Divergence from English

(increased markedness; hyper-articulation)

p → p’

spaal’ → sp’aa

138 (7%)

Hul’q’umi’num’ → Hul’q’umi’num’

(markedness unclear)

hw → xw

lhumuhw → slhumuxw

379 (19%)

Hul’q’umi’num → less marked

(without L1 transfer)

s → ∅

stsuhwum → tsuhwum

28 (1.4%)

Unclear sh → s

shes → shesh

292 (14%)

Table 13 shows that the majority of errors (59%) are ones that converge on English, also leading to less marked forms. This is not surprising, given extensive evidence for this type of effect in L2 pronunciation (Flege, Shirru, and MacKay, 2013 and many others). Nonetheless, 19% of errors involve a change from one Hul’q’umi’num’ sound to another, neither of which also occur in English. Furthermore, an additional 7% of errors are ones that clearly diverge from English, e.g., sound substitutions that go from a shared sound to one that is not found in English. These often also correspond to increases in markedness. While the numbers of ‘diverging’ errors are relatively small,

30


they are not insubstantial. Many of them involve ejectivization and glottalization (3610; see also Table 9). The single most common error was <th> → <lh> (43 errors; see also Table 10).

4.2.4 Summary of errors across phonological properties

Section 4.2 focused on analyzing errors according to the phonological properties of the target sounds and their ‘fixes’. One common type of error was deletion, where an entire segment was left out (Table 8). Glottal stops were primarily deleted in intervocalic position or word-finally, following a vowel. Other consonants were deleted primarily when they occurred in sequences of consonants, either in tautosyllabic initial or final clusters, or in heterosyllabic medial sequences. Other errors involved various kinds of substitutions, in which a sound was replaced by another that differed along one or more phonological dimension(s) (voicing, manner, place). Voicing errors were by far the most common type of substitution error, in particular de-ejectivization and deglottalization (Table 9). Place errors were varied, and often co-occurred with voicing errors (Tables 10 and 12). In particular, errors among ejective coronal stops and affricates commonly involved both a change of place and de-ejectivization. Similarly, uvular ejectives were often fronted and de-ejectivized to velar plain stops. The most common manner errors involved simplification of affricates to either a stop or a fricative, depending on its position (Table 11). Another common error was to strengthen velar and uvular fricatives to stops.

While the majority of errors were compatible with L1 transfer effects and/or markedness effects, this was not the case for all errors (Table 13). The two most common errors that went in the opposite direction were ejectivization, especially in word-initial position (Table 9) and lateralization of the dental fricative, <th> → <lh> (Table 10). These types of errors point to the complexity of sound substitutions, and to the multiple factors that are at play for learners. Given the broad scope of this paper, we were not able to go into any depth explaining the details of sound substitutions, but this is something we hope to tackle next, now that we have the foundation to do so.

5 Discussion and Conclusion

This study provides a broad overview of pronunciation errors that Hul’q’umi’num’ learners tend to make. It is the first study of its kind, with a focus on Coast Salish sounds. Hul’q’umi’num’ is the least endangered of the Coast Salish languages, and has the highest numbers of learners. We are fortunate to have the opportunity to document the pronunciation of these learners, so that we can learn from it and increase the effectiveness of pronunciation instruction both for Hul’q’umi’num’ learners and for learners of other closely related languages. With numbers of Indigenous language learners increasing throughout British Columbia (Dunlop et al., 2018) this kind of research is increasingly relevant.

Our findings show that Hul’q’umi’num’ learners’ pronunciation is affected by a number of factors; some of these are relatively broad (L1 transfer effects; effects of hyper-articulation), and others are more specific (e.g. familiarity of lexical items and sound/sequences, word, syllable, and stress position, phonetic robustness). Many of these factors deserve more in-depth investigation, which is now possible based on the foundation that this broad study has given us. Interestingly, one factor which did not appear to play a role in L2 pronunciation was orthography. Orthography has previously been shown to influence L2 pronunciation (Escudero and Wanrooi, 2010; Hayes-Harb et

10 Note: this number only includes ejectivization/glottalization in cases where the plain counterpart is shared with English. Cases where the plain counterpart is not shared with English were coded as Hul’q’umi’num’ → Hul’q’umi’num’.

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al., 2010). The only pronunciation error that we found some evidence for (admittedly minor) was the pronunciation of as [u] rather than [ə], possibly by analogy to its pronunciation in English words like tune and tube. That very few errors are due to Hul’q’umi’num’ orthographic conventions is a reflection of the fact that learners are able to map Hul’q’umi’num’ letters onto the sounds they represent relatively easily. The neighbouring language SENĆOŦEN is very similar in its sound structure, but uses an entirely different orthography, which includes all capital letters, and a small number of diacritics (e.g. Ŧ represents []). Speakers and learners of SENĆOŦEN have expressed concern that some of the symbols may be affecting learners’ pronunciation, e.g. for [p’], D [t’] and a set of uvular stops represented by a series of <K>s (see Bird and Kell 2015). In the future, it would be interesting to see how similar pronunciation errors are across the two languages, and to what extent differences can be attributed to different orthographic conventions.

With their incredibly rich sound structures, Salish languages provide vast potential in terms of furthering our understanding of the mechanisms that underlie L2 pronunciation. Research in this area is also of critical practical importance in current times, as adult L2 learners take on the responsibility of transmitting their language to future generations, as parents and teachers. We end our paper with a call for increasing research on pronunciation in the context of Indigenous language pedagogy, uniting the fields of Second Language Acquisition, Pronunciation Pedagogy, and Indigenous Language Revitalization.

6 Author Contributions

†These authors have contributed equally to this work and share first authorship

7 Funding

This work was funded by the Social Sciences and Humanities Research Council of Canada, Partnership Development Grant #890-2017-0026.

8 Acknowledgments

We would like to thank Professor Donna Gerdts and the Hul’q’umi’num’ classes of 2016, 2018, and 2019, without whom this research would not be possible. We would also like to thank Professors John Archibald and Li Shih Huang for their insights about second language acquisition and research methods, and Marcus McDermand for checking that our raw data was coded consistently.

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10 Appendix A. IPA - Hul’q’umi’num’ orthography - conversion chart

HUL IPA HUL IPA

a (aa) ɑ (ɑː) s s

e (ee) e (e) sh ʃ


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i (ii) i ( iː) hw xw

u ə x χ

o o xw χw

ou (oo) u~əw h h

ei ej tth tθ

ay aj tth’ tθ’

uy əj ts ts

p p ts’ ts’

p’ p’ tsh tʃ

t t tsh’ tʃ’

t’ t’ tl’ tl’

k k w w

kw kw w’ w’

kw’ kw’ y j


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q q y’ j’

q’ q’ l l

qw qw l’ l’

qw’ qw’ m m

’ ʔ m’ m’

th θ n n

lh ɬ n’ n’

11 Appendix B. Pronunciation tests - word lists

Prontest 1 - 2016

Coronal obstruents

English

tey canoe

stem what

shelh road; door

ts’iit thank them


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tl’am enough

nets’ different

shes sea lion

t’en to go out of sight

stth’am’ bone

tsakw far

slhap’ soup

kw’aant’ dolphin

tth’utth’sh dragonfly

’es-hw seal

smi’mutth’ mash

thu tens his mother

tsiitmuhw owl

wutl’uts’ fall

lemut look


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netulh morning

kw’et’un’ mouse

tumulh ochre

’ushul to paddle

s’itth’um clothes

snuhwulh canoe race

tselush hand

thathun thathun

’ulhtun eat

tsitsulh up above

wetth’ut pry it

sluwi’ cedar bark

lhuthnuts cormorant

tl’elhum salt

slhewun bulrush mat


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tum’xuytl’ winter

p’utth’tun needle

ts’umiil’ thin

sp’atl’um smoke

’ula’ulh on board

haputiil’ cricket

t’un’uthut line up

sququweth rabbit

sxwut’ts’uli hummingbird

t’at’ulhum fierce monster

tthuw’ne’ullh the ones

tthun’ ’imuth your grandson

lhunu sta’lus my wife

Prontest 2 - 2016

Coronal obstruents

English


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swuy’qe’ man

hwulmuhw first nations person

ts’lhhwulmuhw fellow first nations person

xihwu sea urchin

tth’uma’yu barnacle

stsi’elh respected ones

slhelhuq’ lying down

stl’un’uq potlatch

lhts’iws tired

q'e'mi' teen-aged girl

stl’eluqum dangerous, fierce

shts’e’nutstun chair

stseelhtun salmon

xwaaqw’ sawbill; merganser

sxt’ekw’ carving


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sts’uy’hw dry-smoked salmon

sq’i’lu preserved food

stsuw’et clever

qiq'quq'ul's policemen

shch’ekwxul’s frying pan

tth'upsi'athun' squirrel

ts'uw'xilum Mount Tzouhelum

squqw'a'lh mountain goat wool

slhekw'um breath

Prontest - 2018

Animals

English

nuwu one

’een’thu me

spe’uth bear

squl’ew’ beaver


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mousmus cow

sqwumey’ dog

yuxwule’ bald eagle

st’ilum song

smuqw’a heron

swakwun loon

tunuqsun nose

kw’et’un’ mouse

q’ullhanumutsun orca

kwushou pig

sququweth rabbit

tsiitmuhw horned owl

stseelhtun salmon

shes sea lion

stqeeye’ wolf


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mustimuhw person

lelum’ house

lumuhw rain

snuhwulh canoe

sum’shathut sun

thqet tree

spaal’ raven

stsuhwum big wind

tsitsulh up above

t’iwi’ulh pray

Numbers English

nuts’a’ one

yuse’lu two

lhihw three

xu’athun four


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lhq’etsus five

t’xum six

tth’akwus seven

te’tsus eight

toohw nine

’apun ten

pronunciation patterns among l2 hul’q’umi’num’ learners

Documents