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Vocal tract modes based on multiple area function sets from one speaker.

¹Department of Speech, Language, and Hearing Sciences, Speech Acoustics Laboratory, University of Arizona, Tucson, Arizona 85721, USA. bstory@u.arizona.edu

The Journal of the Acoustical Society of America

|April 10, 2009

Summary

This summary is machine-generated.

Researchers derived new vocal tract modes from an expanded set of vowel area functions. This enhanced model provides a wider range of formant frequencies (F1, F2) for improved vocal tract shape analysis.

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Area of Science:

Acoustic Phonetics
Speech Production Modeling
Vocal Tract Acoustics

Background:

Understanding vocal tract shape is crucial for speech production and acoustics.
Previous studies derived vocal tract modes from limited area function datasets.
Principal component analysis has been used to identify key modes of vocal tract variation.

Purpose of the Study:

To derive vocal tract modes from a more comprehensive set of vowel area functions than previously reported.
To generate an expanded formant space ([F1, F2]) using these derived modes.
To assess the utility of the new modes for inverse mapping and vocal tract shape modeling.

Main Methods:

Combined area functions from Story et al. (1996) and Story (2008) into a composite dataset.
Applied principal component analysis to the composite area functions to derive vocal tract modes.
Utilized scaling coefficients and derived modes to generate a two-dimensional formant space ([F1, F2]).

Main Results:

The composite set of area functions yielded a novel set of vocal tract modes.
The formant space generated from the combined modes exhibited a wider range of F1 and F2 values compared to previous methods.
The expanded formant space demonstrates increased coverage of potential vocal tract configurations.

Conclusions:

The newly derived set of vocal tract modes provides a more comprehensive representation of vocal tract shapes.
This expanded set of modes is potentially valuable for inverse mapping of formant frequencies to area functions.
The findings support the use of these modes for more accurate modeling of dynamic vocal tract shape changes during speech.