Adversarial Approximate Inference for Speech to Electroglottograph Conversion

Prathosh AP, Varun Srivastava, Mayank Mishra

Abstract:

Speech produced by human vocal apparatus conveys
substantial non-semantic information including the gender of the
speaker, voice quality, affective state, abnormalities in the vocal
apparatus etc. Such information is attributed to the properties
of the voice source signal, which is usually estimated from the
speech signal. However, most of the source estimation techniques
depend heavily on the goodness of the model assumptions and
are prone to noise. A popular alternative is to indirectly obtain
the source information through the Electroglottographic (EGG)
signal that measures the electrical admittance around the vocal
folds using dedicated hardware. In this paper, we address the
problem of estimating the EGG signal directly from the speech
signal, devoid of any hardware. Sampling from the intractable
conditional distribution of the EGG signal given the speech
signal is accomplished through optimization of an evidence
lower bound. This is constructed via minimization of the KL-
divergence between the true and the approximated posteriors
of a latent variable learned using a deep neural auto-encoder
that serves an informative prior. We demonstrate the efficacy of
the method at generating the EGG signal by conducting several
experiments on datasets comprising multiple speakers, voice
qualities, noise settings and speech pathologies. The proposed
method is evaluated on many benchmark metrics and is found to
agree with the gold standard while proving better than the state-
of-the-art algorithms on a few tasks such as epoch extraction.