2019•The Journal of the Acoustical Society of AmericaRequires access

How the peak glottal area affects linear predictive coding-based formant estimates of vowels

Peter Birkholz, Falk Gabriel, Steffen Kürbis, Matthias Echternach

Open publisher page 17 citations

Abstract

The estimation of formant frequencies from acoustic speech signals is mostly based on Linear Predictive Coding (LPC) algorithms. Since LPC is based on the source-filter model of speech production, the formant frequencies obtained are often implicitly regarded as those for an infinite glottal impedance, i.e., a closed glottis. However, previous studies have indicated that LPC-based formant estimates of vowels generated with a realistically varying glottal area may substantially differ from the resonances of the vocal tract with a closed glottis. In the present study, the deviation between closed-glottis resonances and LPC-estimated formants during phonation with different peak glottal areas has been systematically examined both using physical vocal tract models excited with a self-oscillating rubber model of the vocal folds, and by computer simulations of interacting source and filter models. Ten vocal tract resonators representing different vowels have been analyzed. The results showed that F1 increased with the peak area of the time-varying glottis, while F2 and F3 were not systematically affected. The effect of the peak glottal area on F1 was strongest for close-mid to close vowels, and more moderate for mid to open vowels.

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What this paper is about

The estimation of formant frequencies from acoustic speech signals is mostly based on Linear Predictive Coding (LPC) algorithms. Since LPC is based on the source-filter model of speech production, the formant frequencies obtained are often implicitly regarded as those for an infinite glottal impedance, i.e., a closed glottis. However, previous studies have indicated that LPC-based formant estimates of vowels generated with a realistically varying glottal area may substantially differ from the resonances of the vocal tract with a closed glottis. In the present study, the deviation between closed-glottis resonances and LPC-estimated formants during phonation with different peak glottal areas has been systematically examined both using physical vocal tract models excited with a self-oscillating rubber model of the vocal folds, and by computer simulations of interacting source and filter models. Ten vocal tract resonators representing different vowels have been analyzed. The results showed that F1 increased with the peak area of the time-varying glottis, while F2 and F3 were not systematically affected. The effect of the peak glottal area on F1 was strongest for close-mid to close vowels, and more moderate for mid to open vowels.

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Available abstract

The estimation of formant frequencies from acoustic speech signals is mostly based on Linear Predictive Coding (LPC) algorithms. Since LPC is based on the source-filter model of speech production, the formant frequencies obtained are often implicitly regarded as those for an infinite glottal impedance, i.e., a closed glottis. However, previous studies have indicated that LPC-based formant estimates of vowels generated with a realistically varying glottal area may substantially differ from the resonances of the vocal tract with a closed glottis. In the present study, the deviation between closed-glottis resonances and LPC-estimated formants during phonation with different peak glottal areas has been systematically examined both using physical vocal tract models excited with a self-oscillating rubber model of the vocal folds, and by computer simulations of interacting source and filter models. Ten vocal tract resonators representing different vowels have been analyzed. The results showed that F1 increased with the peak area of the time-varying glottis, while F2 and F3 were not systematically affected. The effect of the peak glottal area on F1 was strongest for close-mid to close vowels, and more moderate for mid to open vowels.

Key concepts: Glottis, Formant, Vocal tract, Phonation, Acoustics, Vowel, Speech production, Linear predictive coding

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