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arXiv · 2303.12187

Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English

Xiaoming Ren·Chao Li·Shenjian Wang·Biao Li
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Abstract

Considering the bimodal nature of human speech perception, lips, and teeth movement has a pivotal role in automatic speech recognition. Benefiting from the correlated and noise-invariant visual information, audio-visual recognition systems enhance robustness in multiple scenarios. In previous work, audio-visual HuBERT appears to be the finest practice incorporating modality knowledge. This paper outlines a mixed methodology, named conformer enhanced AV-HuBERT, boosting the AV-HuBERT system's performance a step further. Compared with baseline AV-HuBERT, our method in the one-phase evaluation of clean and noisy conditions achieves 7% and 16% relative WER reduction on the English AVSR benchmark dataset LRS3. Furthermore, we establish a novel 1000h Mandarin AVSR dataset CSTS. On top of the baseline AV-HuBERT, we exceed the WeNet ASR system by 14% and 18% relatively on MISP and CMLR by pre-training with this dataset. The conformer-enhanced AV-HuBERT we proposed brings 7% on MISP and 6% CER reduction on CMLR, compared with the baseline AV-HuBERT system.

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Xiaoming Ren, Chao Li, Shenjian Wang, Biao Li. 2023-02-28. Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English. https://arxiv.org/abs/2303.12187

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