2022
DOI: 10.48550/arxiv.2204.07075
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Learning and controlling the source-filter representation of speech with a variational autoencoder

Samir Sadok,
Simon Leglaive,
Laurent Girin
et al.

Abstract: Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are produced from a few independent and physically meaningful continuous latent factors, among which the fundamental frequency f 0 and the formants are of primary importance. In this work, we show tha… Show more

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