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Set Features for Fine-grained Anomaly Detection
Audio-based automatic speech recognition (ASR) degrades significantly in
noisy environments and is particularly vulnerable to interfering speech, as the
model cannot determine which speaker to transcribe. Audio-visual speech
recognition (AVSR) systems improve robustness by complementing the audio stream
with the visual information that is invariant to noise and helps the model
focus on the desired speaker. However, previous AVSR work focused solely on the
supervised learning setup; hence the progress was hindered by the amount of
labeled data available. In this work, we present a self-supervised AVSR
framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art
audio-visual speech representation learning model. On the largest available
AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by
~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in
the presence of babble noise, while reducing the WER of an audio-based model by
over 75% (25.8% vs. 5.8%) on average.
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