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Connecting Acoustics to Linguistics in Chinese Intonation Greg Kochanski (Oxford Phonetics) Chilin Shih (University of Illinois) Tan Lee (CUHK) with Hongyan Jing (IBM) Jiahong Yuan (Cornell)

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Questions Can we usefully include biomechanics into a phonetics model? Can we objectively assign an importance to a syllable? Can we write a unified description of F 0 for both tone and accent languages? Goal Build a mathematical model that takes a sequence of discrete symbols as input and produces a quantitative prediction for f0.

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The Challenge

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Existing work Rising?

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Basic assumptions used in modeling People plan their utterances several syllables in advance. People produce speech optimized to communicate with minimal effort. A realistic model for the muscles that control f 0

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Realistic model of muscle control for F 0 We’d like a model of prosody that can apply beyond F 0.

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People talk nearly as fast as possible.

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Speech could be optimal Most of what we say is made from bits and pieces we’ve said before. There are only 4 (Mandarin) or 6 (Cantonese) tones to combine. A speaker has the chance to practice and optimize all the common 3- and 4- tone sequences.

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Optimize what? People want to minimize effort and/or talk faster –Chairs, Cars People want to minimize the chance that they will be misunderstood. –Risk = P(misinterpreted) * cost(misinterpreted) Minimize: Effort + cost*Error –We allow each syllable to have a different weight, so error is a sum over syllables or words. –Perhaps cost matches importance.

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Modeling math is the muscle tension (~frequency) at time t. “Effort” Each target encodes some linguistic information, r i is the error of the i th target, and s i is its importance. y is the i th pitch target and a bar denotes an average over a target. “Error”

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Effort and Error How does Effort depend on the form of the pitch curve? Error = mean-squared deviation between the f0 and the templates.

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Model behavior For cost>>1, Error dominates, and pitch matches target. For cost<<1, Effort dominates, both speaker and listener accept large deviations, and pitch smoothly interpolates. For cost~1, everything compromises. Cost plays the role of a prosodic strength.

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Another Challenge Time (10 ms intervals) F 0 (Hz) 1 2 3 4 Tone shapes

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The rest of the model. A model is a sequence of targets (used to compute the Error terms). Each target has a strength (i.e. the cost of misinterpretation). One target per tone. Targets are stretched to fit syllable duration. Only one phonological rule: 33 23

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Model fits for Mandarin Chinese Tone class (input) Strength (result) Inside a word, strength is distributed by the metrical pattern

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What’s the procedure? Compute the pitch curve as a function of phonological inputs and prosodic strength. Sequence of tones (phonology) Prosodic strengths Predicted F 0 Data Nonlinear least-squares fitting algorithm

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Model fits to Mandarin Chinese 0.61 free parameters per syllable, 13 Hz RMS error.

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Strengths are stable under small changes in the model. The two models have words defined by different labelers This model allows extra freedom: different tones are allowed to define their targets differently This model allows less freedom: all tones have the same type of target.

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Model parameters Mandarin Cantonese Phrasing is marked in speech. Cantonese data courtesy of Prof. Tan Lee

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Model parameters Cantonese Mandarin Nouns are relatively important.

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Model parameters Cantonese Mandarin Longer words tend to be spoken more carefully.

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Metrical patterns inside words Mandarin “Normal” segmentation of characters into words. Random segmentation of characters into words. Lexical acquisition

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Other nice properties Strengths are correlated with duration: (duration is a proxy for prominence) r = 0.40 (sentence final) r = 0.27 (non-final) >95% confidence Strength is correlated with mutual information of neighboring syllables: r = -0.175 >95% confidence Sloppy when generating unsurprising syllables, and precise for surprising syllables.

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Local Conclusion Intonation can be represented as: – a small set of discrete symbols, in sequence, with –a per-person or per-style shape for each symbol; –modulated by a variable prosodic strength. One symbol per syllable seems enough The strength parameter seems real –Similar across languages –Matches language structure

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Q: But does it work for English? A: Yes, under circumstances where the intonational phonology is simple enough to be obvious.

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Reminder: Limitations of f 0 and complexity of prosody. To show the range of information that can be carried by prosody, observe an elegant experiment by Stan Freberg (1950): The text has virtually no lexical information, but it still tells a story. Even so, it is very hard to label individual words.

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English Sentences in the form “123-456-7890?” Speaker is trying to confirm a single digit. Models have just 1.1 parameter per sentence.

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The model for English There are identical boundary tones on every utterance. All target shapes are identical, except the focus. %X B B B | B A B | B B B B Y% %X B B B | A B B | B B B B Y% %X B A B | B B B | B B B B Y% Rather simple phonology. Accent prominence depends on position in phrase and in utterance.

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Model details Strength time 910 – 999 - 1010 Decline over utterance Decline over phrase Local effect around accent Compress range after accent

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The rest of the model. Where do you put the targets? What are the targets? –Pitch values? –Slopes? Do the targets change in f 0 range with changes in strength?

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Model fits well over a range of speeds. Suppressed phrasing Low speed High speed Merger of accent with boundary tone

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Model reproduces nontrivial features of the data and fits well over a range of speeds. Suppressed phrasing Low speed High speed Merger of accent with boundary tone

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Conclusion Physiologically-based models can capture important aspects of speech. A very compact representation of behavior. It can be applied broadly: Two dialects of Chinese Some aspects of English It raises questions about where the phonetics/phonology boundary actually sits. Introduces an objective acoustic measure of prosodic prominence. Suggests that the speaker may help the listener segment the speech stream.

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