u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality

Wei-Ning Hsu · Bowen Shi

Hall J #214

Keywords: [ speech translation ] [ audio-visual speech ] [ multimodal self-supervised learning ] [ multimodal speech ] [ Zero-Shot ] [ Speech Recognition ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Wed 30 Nov 2 p.m. PST — 4 p.m. PST


While audio-visual speech models can yield superior performance and robustness compared to audio-only models, their development and adoption are hindered by the lack of labeled and unlabeled audio-visual data and the cost to deploy one model per modality. In this paper, we present u-HuBERT, a self-supervised pre-training framework that can leverage both multimodal and unimodal speech with a unified masked cluster prediction objective. By utilizing modality dropout during pre-training, we demonstrate that a single fine-tuned model can achieve performance on par or better than the state-of-the-art modality-specific models. Moreover, our model fine-tuned only on audio can perform well with audio-visual and visual speech input, achieving zero-shot modality generalization for multiple speech processing tasks. In particular, our single model yields 1.2%/1.4%/27.2% speech recognition word error rate on LRS3 with audio-visual/audio/visual input.

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