  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.
