Evaluation of speech inverse filtering techniques using a physiologically based synthesizer

Glottal inverse filtering methods are designed to derive a glottal flow waveform from a speech signal. In this paper, we evaluate and compare such methods using a speech synthesizer that simulates voice production in a physiologically-based manner that includes complexities such as nonlinear source-tract coupling. Five inverse filtering techniques are evaluated on 90 synthesized speech waveforms generated by setting six vowel configurations, three glottal models, and five fundamental frequencies. Using normalized mean square error as the primary performance metric of the estimated glottal flow derivative, results show that the accuracy of all methods depends on the configuration of the vocal tract, glottis and the fundamental frequency. Averaged over these conditions, the closed phase covariance and one weighted covariance algorithm yield lower error rates (0.41 ± 0.2) than iterative and adaptive inverse filtering (0.49 ± 0.1) and complex cepstrum decomposition (0.76 ± 0.1).