sonority as a basis for rhythmic class discrimination
DESCRIPTION
Sonority as a Basis for Rhythmic Class Discrimination. Antonio Galves, USP. Jesus Garcia, USP. Denise Duarte, USP and UFGo. Charlotte Galves, UNICAMP. The starting point : Ramus, Nespor & Mehler (1999). What we do. - PowerPoint PPT PresentationTRANSCRIPT
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Sonority as a Basis for Rhythmic Class Discrimination
Antonio Galves, USP.
Jesus Garcia, USP.
Denise Duarte, USP and UFGo.
Charlotte Galves, UNICAMP.
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The starting point : Ramus, Nespor & Mehler (1999)
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What we do
• Our goal: a new approach to the problem of finding acoustic correlates of the rhythmic classes.
• Main ingredient: a rough measure of sonority defined directly from the spectrogram of the signal.
• Major advantage: can be implemented in an entirely automatic way, with no need of previous hand-labelling of the acoustic signal.
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Our main result
Applied to the same linguistic samples considered in RNM, our approach produces the same clusters corresponding to the three conjectured rhythmic classes.
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RNM revisited
Striking features
• Linear correlation between ΔC and %V (-0.93).
• Clustering into three groups.
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A parametric probabilistic model for RNM
Duarte et al. (2001) propose a parametric family of probability distributions that closely fit the data in RNM.
This has two advantages:• It provides a deeper insight of the phenomena.• It makes it possible to perform statistical
inference, i-e to extend results from the sample (data set) to the population (the set of all potential sentences).
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The probabilistic model
• The duration of the successive consonantal intervals are independent and identically distributed random variables.
• The duration of each consonantal interval is distributed acording to a Gamma distribution.
• Different languages have Gamma distributions with different standard deviations.
• The standard deviation is constant for all languages belonging to the same rhythmic class.
• The standard deviations of different classes are different.
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Statistical evidence for the clustering
• The model enables testing the hypothesis that the eight languages are clustered in three groups.
• The hypothesis that the standard deviations of the Gamma distributions are constant within classes and differ among classes are compatible with the data presented in RNM.
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Estimated standard deviations of the Gamma distribution for the consonantal intervals
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Problems for RNM (1)
• RNM is based on a hand-labeling segmentation which is time-consuming and depends on decisions which are difficult to reproduce in an homogeneous way.
• This is a problem for linguists.
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Problems for RNM (2)
• Newborn babies discriminate rhythmic groups from signal filtered at 400 Hz (Mehler et al. 1996). At this frequency, it is impossible to fully discriminate consonants and vowels.
• ΔC depends on a complex computation.
• This is a problem for babies!
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Sonority as a basis for rhythmic class discrimination
• Mehler et al. (1996)’s results strongly suggest that the discrimination of rhythmic classes by babies relies not on a fine-grained distinction between vowels and consonants, but on a coarse-grained perception of sonority in opposition to obstruency.
• A natural conjecture is that the identification of rhythmic classes must be possible using a rough measure of sonority.
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A rough measure of sonority
Goal: to define a function that maps local windows of the signal on the interval [0,1]. This function should assign
• values close to 1 for spans displaying regular patterns, characteristic of the sonorant regions of the signal,
• values close to 0 for regions characterized by high obstruency.
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Technical specifications
• The function s(t) is based on the spectrogram of the signal.
• Values of the spectrogram are estimated with a 25ms Gaussian window.
• The step unit of the function is 2ms.
• Computations are made with Praat (http://www.praat.org)
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Definition of the function s(t)
pt(f) = re-normalized power spectrum for frequency f around time t.This re-normalization makes pt a probability measure.A regular pattern characteristic of sonorant spans will produce a sequence of probability measures which are close in the sense of relative entropy. This suggests defining the function sonority as
4
4
3
1
)|(27
1,1min1)(
t
tu iiuu pphts
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Values of 1- s(t) on a Japanese example
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Estimators
T
t
tstsT
S1
)1()(1
T
t
tsT
S1
)(1
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Explaining the estimators
• is the sample mean of the function s(t).
• δS measures how important are the high
obstruency regions in the sample. This is due to the fact that typically the values of p(t), and consequently s(t), present large variations when t belongs to intervals with high obstruency.
S
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Distribution of the eight languages on the ( ,S) plane
S
S
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Extra statistical features
Languages P(s<0.3) Q3-Q1
Japanese 0.170 0.47
Catalan 0.177 0.47
Italian 0.182 0.48
Spanish 0.205 0.54
French 0.210 0.53
Polish 0.218 0.58
English 0.234 0.60
Dutch 0.254 0.64
• The distance between the first and third quartile increases from Japanese to Dutch. In other terms, the dispersion of sonority increases from mora-timed to stress-timed languages.
• The empirical probability of having sonority smaller than 0.3 also increases from Japanese to Dutch.
• This reinforces the idea present in Duarte et al. (2001) that the relevant information to discriminate among rhythmic classes is contained in the less sonorant part of the signal.
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Distribution of the eight considered languages on the ( ,%V) planeS
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Distribution of the eight languages on the (S,C) plane
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Conclusions
• The main purpose of this presentation was to show that the relevant evidence about rhythmic classes can be automatically retrieved from the acoustic signal, through a rough measure of sonority.
• In addition, our statistics are based on a coarse-grained treatment of the speech signal which is likely to be closer to the linguistic reality of the early acquisition.
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• This work is part of the Project RHYTHMIC PATTERNS, PARAMETER SETTING AND LANGUAGE CHANGE, funded by Fapesp (grant no 98/03382-0).
• http://www.ime.usp.br/~tycho
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Values of 1- s(t) on a Dutch example
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Values of 1- s(t) on a French example