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.
Cite as: Kadiri, S.R., Alku, P. (2019) Mel-Frequency Cepstral Coefficients of Voice Source Waveforms for Classification of Phonation Types in Speech. Proc. Interspeech 2019, 2508-2512, doi: 10.21437/Interspeech.2019-2863
@inproceedings{kadiri19_interspeech,
author={Sudarsana Reddy Kadiri and Paavo Alku},
title={{Mel-Frequency Cepstral Coefficients of Voice Source Waveforms for Classification of Phonation Types in Speech}},
year=2019,
booktitle={Proc. Interspeech 2019},
pages={2508--2512},
doi={10.21437/Interspeech.2019-2863}
}