mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition

Andrew Rouditchenko; Samuel Thomas; Hilde Kuehne; Rogerio Feris; James Glass

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arXiv · 2502.01547

mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition

Andrew Rouditchenko·Samuel Thomas·Hilde Kuehne·Rogerio Feris·James Glass

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Abstract

Audio-Visual Speech Recognition (AVSR) combines lip-based video with audio and can improve performance in noise, but most methods are trained only on English data. One limitation is the lack of large-scale multilingual video data, which makes it hard to train models from scratch. In this work, we propose mWhisper-Flamingo for multilingual AVSR which combines the strengths of a pre-trained audio model (Whisper) and video model (AV-HuBERT). To enable better multi-modal integration and improve the noisy multilingual performance, we introduce decoder modality dropout where the model is trained both on paired audio-visual inputs and separate audio/visual inputs. mWhisper-Flamingo achieves state-of-the-art WER on MuAViC, an AVSR dataset of 9 languages. Audio-visual mWhisper-Flamingo consistently outperforms audio-only Whisper on all languages in noisy conditions.

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Andrew Rouditchenko, Samuel Thomas, Hilde Kuehne, Rogerio Feris, James Glass. 2025-02-03. mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition. https://arxiv.org/abs/2502.01547

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