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2019

AV Speech Enhancement Challenge using a Real Noisy Corpus

Gogate, Mandar, Adeel, Ahsan, Dashtipour, Kia et al.

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This paper presents, a first of its kind, audio-visual (AV) speech enhacement challenge in real-noisy settings.

A detailed description of the AV challenge, a novel real noisy AV corpus (ASPIRE), benchmark speech enhancement task, and baseline performance results are outlined.
The latter are based on training a deep neural architecture on a synthetic mixture of Grid corpus and ChiME3 noises (consisting of bus, pedestrian, cafe, and street noises) and testing on the ASPIRE corpus.
Subjective evaluations of five different speech enhancement algorithms (including SEAGN, spectrum subtraction (SS) , log-minimum mean-square error (LMMSE), audio-only CochleaNet, and AV CochleaNet) are presented as baseline results.

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