Bootstrapping Audiovisual Speech Recognition in Zero-AV-Resource Scenarios

Abstract:Audiovisual speech recognition (AVSR) combines acoustic and visual cues to improve transcription robustness under challenging conditions but remains out of reach for most under-resourced languages due to the lack of labeled video corpora for training. Synthetic visual data have been shown to be an effective augmentation strategy for addressing AV data scarcity. However, a more challenging scenario arises for languages such as Catalan, where no real audiovisual data are available for training. In this study, we investigate whether AVSR can be bootstrapped in such a zero-AV-resource setting, using synthetic visual data as the sole source of visual supervision. We synthesize over 700 hours of talking-head video and fine-tune a pre-trained AV-HuBERT model. On a manually annotated Catalan benchmark, our model achieves near state-of-the-art (SOTA) performance with much fewer parameters and training data than SOTA ASR systems such as Whisper-large-v3, outperforms an identically trained audio-only baseline, and preserves multimodal advantages under acoustic degradation. Scalable synthetic video thus offers a viable substitute for real recordings in zero-AV-resource AVSR.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Image and Video Processing (eess.IV)

arXiv:2603.08249 [eess.AS]

(or arXiv:2603.08249v2 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2603.08249 Focus to learn more arXiv-issued DOI via DataCite

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