Aldana Blanco, Andrea Lorena

Bell, Peter

Dashtipour, Kia

Gogate, Mandar

Hussain, Amir

Klejch, Ondrej

Valentini Botinhao, Cassia

English

Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too challenging for audio-only speech enhancement models. The Audio-Visual Speech Enhancement (AVSE) challenge aims to set the first benchmark in this area. We provide participants with datasets and scripts to test their audio-visual speech enhancement models under a common framework for both training and evaluation. The data is derived from real-world videos, and comprises noisy mixes, in which audio from target speaker is mixed with either a competing speaker or a noise signal. The submitted systems are evaluated by conducting AV intelligibility tests involving human participants. We expect this challenge to be a platform for advancing the field of audio-visual speech-enhancement and to provide further insight about the scope and limitations of current AV speech enhancement approaches

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AVSE Challenge: Audio-Visual Speech Enhancement Challenge

https://www.research.ed.ac.uk/files/305863115/AVSE_Challenge_ALDANA_DOA30092022_AFV.pdf

AVSE Challenge: Audio-Visual Speech Enhancement Challenge

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