Practice of the Conformer Enhanced Audio-Visual Hubert on Mandarin and English
Xiaoming Ren, Chao Li, Shenjian Wang, Biao Li
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.
BibTeX
@inproceedings{icassp2023_practiceofthecon,
title = {Practice of the Conformer Enhanced Audio-Visual Hubert on Mandarin and English},
author = {Xiaoming Ren and Chao Li and Shenjian Wang and Biao Li},
booktitle = {ICASSP 2023},
year = {2023}
}