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Audio-Visual Speech Recognition is Worth 32x32x8 Voxels

Dmitriy (Dima) Serdyuk
ASRU (2021)
Google Scholar


Audio-visual automatic speech recognition (AV-ASR) introduces the video modality into the speech recognition process, in particular often relying on information conveyed by the motion of the speaker's mouth. The use of the visual signal requires extracting visual features, which are then combined with the acoustic features to build an AV-ASR system~\cite{Makino2019-zd}. This is traditionally done with some form of 3D convolution network (e.g. VGG) as widely used in the computer vision community. Recently, video transformers~\cite{Dosovitskiy2020-nh} have been introduced to extract visual features useful for image classification tasks. In this work, we propose to replace the 3D convolution visual frontend typically used for AV-ASR and lip-reading tasks by a video transformer frontend. We train our systems on a large-scale dataset composed of YouTube videos and evaluate performance on the publicly available LRS3-TED set, as well as on a large set of YouTube videos. On a lip-reading task, the transformer-based frontend shows superior performance compared to a strong convolutional baseline. On an AV-ASR task, the transformer frontend performs as well as a VGG frontend for clean audio, but outperforms the VGG frontend when the audio is corrupted by noise.

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