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Sonority as a Basis for Rhythmic Class DiscriminationPowerPoint Presentation

Sonority as a Basis for Rhythmic Class Discrimination

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Sonority as a Basis for Rhythmic Class Discrimination

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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.

- 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.

Applied to the same linguistic samples considered in RNM, our approach produces the same clusters corresponding to the three conjectured rhythmic classes.

Striking features

- Linear correlation between ΔC and %V (-0.93).
- Clustering into three groups.

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).

- The durationof 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.

- 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.

- 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.

- 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!

- 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.

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.

- 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)

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

- 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.

- 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.

- 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.

- 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