On the Use of a Spectral Glottal Model for the Source-filter Separation of Speech

TL;DR

This paper introduces GFM-IAIF, an enhanced spectral glottal model that improves source-filter separation in speech by capturing both glottal formant and spectral tilt, leading to better perception of vocal effort.

Contribution

The paper presents GFM-IAIF, an improved inverse filtering method that models the full spectral characteristics of the glottis, including spectral tilt.

Findings

GFM-IAIF maintains effective vocal tract removal.

It significantly enhances perception of vocal effort.

Outperforms standard IAIF in spectral tilt estimation.

Abstract

The estimation of glottal flow from a speech waveform is a key method for speech analysis and parameterization. Significant research effort has been made to dissociate the first vocal tract resonance from the glottal formant (the low-frequency resonance describing the open-phase of the vocal fold vibration). However few methods cope with estimation of high-frequency spectral tilt to describe the return-phase of the vocal fold vibration, which is crucial to the perception of vocal effort. This paper proposes an improved version of the well-known Iterative Adaptive Inverse Filtering (IAIF) called GFM-IAIF. GFM-IAIF includes a full spectral model of the glottis that incorporates both glottal formant and spectral tilt features. Comparisons with the standard IAIF method show that while GFM-IAIF maintains good performance on vocal tract removal, it significantly improves the perceptive timbral variations associated to vocal effort.