Audio-visual automatic speech recognition (AV-ASR) models are very effective
at reducing word error rates on noisy speech, but require large amounts of
transcribed AV training data. Recently, audio-visual self-supervised learning
(SSL) approaches have been developed to reduce this dependence on transcribed
AV data, but these methods are quite complex and computationally expensive. In
this work, we propose replacing these expensive AV-SSL methods with a simple
and fast \textit{audio-only} SSL method, and then performing AV supervised
fine-tuning. We show that this approach is competitive with state-of-the-art
(SOTA) AV-SSL methods on the LRS3-TED benchmark task (within 0.5% absolute
WER), while being dramatically simpler and more efficient (12-30x faster to
pre-train). Furthermore, we show we can extend this approach to convert a SOTA
audio-only ASR model into an AV model. By doing so, we match SOTA AV-SSL
results, even though no AV data was used during pre-training.

使用简单且快速的音频自我监督学习方法，并进行音视频模型的有指导微调，可在减少大量文本数据依赖的同时与最先进的音视频自我监督学习方法竞争，并且更为高效和快速。