A Spectral Glottal Flow Model for Source-filter Separation of Speech

Olivier Perrotin 1 Ian Mcloughlin 2
1 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : The estimation of glottal flow from a speech waveform is an essential technique used in speech analysis and parameterisa-tion. Significant research effort has been addressed at separating the first vocal tract resonance from the glottal formant (the low-frequency resonance that describes the open-phase of the vocal fold vibration), but few methods are capable of estimating the high-frequency spectral tilt, characteristic of the closing phase of the vocal fold vibration (which is crucial to the perception of vocal effort). This paper proposes an improved Iterative Adaptive Inverse Filtering (IAIF) method based on a Glottal Flow Model, which we call GFM-IAIF. The proposed method models the wide-band glottis response, incorporating both glottal formant and spectral tilt characteristics. Evaluation against IAIF and recently proposed IOP-IAIF shows that, while GFM-IAIF maintains good performance on vocal tract modelling, it significantly improves the glottis model. This ensures that timbral variations associated to voice quality can be correctly attributed and described.
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Olivier Perrotin, Ian Mcloughlin. A Spectral Glottal Flow Model for Source-filter Separation of Speech. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.7160-7164, ⟨10.1109/ICASSP.2019.8682625⟩. ⟨hal-02106450⟩

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