rudy marsman's thesis presentation slides: speech synthesis based on a limited speech corpus
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Speech synthesis based on a limited speech
corpusRudy Marsman | VU University | NISV
Netherlands Institute for Sound and Vision (NISV) | Beeld & Geluid
Beeld en Geluid
• collects, preserves and opens the Dutch audiovisual heritage for as many users as possible• one of the largest audiovisual archives in Europe. The
institute manages over 70 percent of the Dutch audiovisual heritage• Was interested in ways to re-use old Polygoonjournaals
footage• Text-To-Speech engine based on Philip Bloemendal
Philip Bloemendal
• Famous anchorman• Iconic voice• https://www.youtube.com/watch?v=31tClHJ2tfQ
Research
• Can the current corpus of audio recordings of Bloemendal be used to construct a TTS engine?• How large percentage of the Dutch language can be constructed
with the current corpus?• What can we do to improve?• How well is the text-to-speech engine recognizable as Philip
Bloemendal?• How well comprehensive are the constructed audiofiles?
How large percentage of the Dutch language can be constructed with the current corpus?
• Constructing the corpus• How many ‘Polygoonjournaals’ • Openbeelden – OAI (Open Archives Initiative)• Extract audio• Speech analysis – roughly 35000 distinct words • XML files
• Evaluation• Metrics• Corpora• Language changes
How large percentage of the Dutch language can be constructed with the current corpus?
• Approach: 4 corpora to test against• Contemporary news articles (same domain, different time) | 50
articles• News articles from the 1970s (same domain, time) | 50 articles• E-books (different domain, various times) |6 books• Tweets (different domain, different time) | 1000 tweets
• Evaluation• Number of distinct words• Number of sentences
What can we do to improve performance?
• It is to be expected that many (contemporary) words have not been pronounced by Philip• Various approaches
• Change format (Lowercase, diareses)• Numbers• Finding synonyms• Decompounding
Finding Synonyms
• Open Dutch Wordnet: Dutch lexical semantic database• Maarten Postma et al.• Yields synsets (e.g. Hoofdmeester -> Rector, Schoolhoofd)• Computationally expensive
Decompounding
• Dutch language allows for compounding words• School, hoofd -> Schoolhoofd• Regen, water -> regenwater• Staat, hoofd -> StaatShoofd
• Each word is distinct in the corpus• Decompounding is computationally expensive• Computationally expensive for large corpora, long words• Constructed Bigrams and Trigrams
Results (words)
Dataset Unique words
Unique words found
After synsets After decompounding
Contemporary news
2743 2019 2106 2448
Old news 16191 7703 8261 11541Tweets 27180 7692 8446 13440Books 26575 11440 12922 20207
Results (sentences)
Dataset Unique sentences
Unique sentences found
After synsets After decompounding
Contemporary news
1022 106 110 186
Old news 2626 183 190 301Tweets 8937 174 181 296Books 56106 9387 11385 18271
How comprehensible / recognizable are sentences• 8 people tested the software• Philip was recognized (or ‘that news guy’)• Words with more consonants were easier to recognize• When user input their own sentences, more recognition• When sentences were demonstrated without subtitles, less• Speed of software / GUI limited testing capabilities
The use of Deep Neural Networks in colorizing
videoRudy Marsman | VU University | NISV
Neural Networks
• Recent progress in computational power made implementation of Deep Neural Nets possible• Neural Net trained on large training set can accurately
make predictions in real-world examples
Zhang et al.
• Richard Zhang et al. trained a neural net to colorize images• Trained on over a million images• Fools humans into thinking colorized photo is original 20%
of time• Resizes image to fit input layer of 200x200 pixels• Gained popularity in news website / forums
Zhang et al.
Implementation on video
• Extract individual frames from video using FFMPEG• Colorize each individual frame• Re-compile video and attach original audio file
Example
• https://www.youtube.com/watch?v=olsO2rOy_i4
Applications
• Colorized videos are more ‘tangible’ and ‘alive’ than black/white• Showing colorized Polygoonjournaals can augment TTS
engine• General positive responses on technology may increase
attention to NISV collection• NISV Employees were enthousiastic
Issues
• Each frame is considered independent and is colorized thusly• Artifacts appear between frames• Slow performance without use of Nvidia GPU• Low resolution• Predicted colors still far from perfect
Conclusions
• Current corpus covers many of often used words• Various implemented approacheds increase coverage• Low coverage for sentences -> real world approach may
need improvement• Audio is recognizable and understandable• Neural Networks may be used to colorize video footage
Discussion