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Abstract
Voice source characteristics in different phonation types vary due to the tension of laryngeal muscles along with the respiratory effort. This study investigates the use of mel-frequency cepstral coefficients (MFCCs) derived from voice source waveforms for classification of phonation types in speech. The cepstral coefficients are computed using two source waveforms: (1) glottal flow waveforms estimated by the quasi-closed phase (QCP) glottal inverse filtering method and (2) approximate voice source waveforms obtained using the zero frequency filtering (ZFF) method. QCP estimates voice source waveforms based on the source-filter decomposition while ZFF yields source waveforms without explicitly computing the source-filter decomposition. Experiments using MFCCs computed from the two source waveforms show improved accuracy in classification of phonation types compared to the existing voice source features and conventional MFCC features. Further, it is observed that the proposed features have complimentary information to the existing features.
Original language | English |
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Title of host publication | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
Publisher | International Speech Communication Association (ISCA) |
Pages | 2508-2512 |
Number of pages | 5 |
Volume | 2019-September |
DOIs | |
Publication status | Published - 1 Jan 2019 |
MoE publication type | A4 Conference publication |
Event | Interspeech - Graz, Austria Duration: 15 Sept 2019 → 19 Sept 2019 https://www.interspeech2019.org/ |
Publication series
Name | Interspeech - Annual Conference of the International Speech Communication Association, INTERSPEECH |
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ISSN (Electronic) | 2308-457X |
Conference
Conference | Interspeech |
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Country/Territory | Austria |
City | Graz |
Period | 15/09/2019 → 19/09/2019 |
Internet address |
Keywords
- Glottal inverse filtering
- Phonation type
- Speech analysis
- Voice quality
- Voice source
- Zero frequency filtering
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Dive into the research topics of 'Mel-frequency cepstral coefficients of voice source waveforms for classification of phonation types in speech'. Together they form a unique fingerprint.Projects
- 1 Finished
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Interdisciplinary research on statistical parametric speech synthesis
Alku, P. (Principal investigator)
01/01/2018 → 31/12/2019
Project: Academy of Finland: Other research funding