  Most methods of voice restoration for patients suffering from aphonia either
produce whispered or monotone speech. Apart from intelligibility, this type of
speech lacks expressiveness and naturalness due to the absence of pitch
(whispered speech) or artificial generation of it (monotone speech). Existing
techniques to restore prosodic information typically combine a vocoder, which
parameterises the speech signal, with machine learning techniques that predict
prosodic information. In contrast, this paper describes an end-to-end neural
approach for estimating a fully-voiced speech waveform from whispered
alaryngeal speech. By adapting our previous work in speech enhancement with
generative adversarial networks, we develop a speaker-dependent model to
perform whispered-to-voiced speech conversion. Preliminary qualitative results
show effectiveness in re-generating voiced speech, with the creation of
realistic pitch contours.

Sound;Machine Learning;Audio and Speech Processing