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Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation

2022-04-05 15:02:49
Marc-Antoine Georges, Julien Diard, Laurent Girin, Jean-Luc Schwartz, Thomas Hueber
arXiv_CL
       
Abstract

We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpretable articulatory parameters, a DNN-based internal forward model predicting the sensory consequences of articulatory commands, and an internal inverse model based on a recurrent neural network recovering articulatory commands from the acoustic speech input. Both forward and inverse models are jointly trained in a self-supervised way from raw acoustic-only speech data from different speakers. The imitation simulations are evaluated objectively and subjectively and display quite encouraging performances.

Abstract (translated)

URL

https://arxiv.org/abs/2204.02269

PDF

https://arxiv.org/pdf/2204.02269.pdf

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