  The growing prevalence of online conferences and courses presents a new
challenge in improving automatic speech recognition (ASR) with enriched textual
information from video slides. In contrast to rare phrase lists, the slides
within videos are synchronized in real-time with the speech, enabling the
extraction of long contextual bias. Therefore, we propose a novel long-context
biasing network (LCB-net) for audio-visual speech recognition (AVSR) to
leverage the long-context information available in videos effectively.
Specifically, we adopt a bi-encoder architecture to simultaneously model audio
and long-context biasing. Besides, we also propose a biasing prediction module
that utilizes binary cross entropy (BCE) loss to explicitly determine biased
phrases in the long-context biasing. Furthermore, we introduce a dynamic
contextual phrases simulation to enhance the generalization and robustness of
our LCB-net. Experiments on the SlideSpeech, a large-scale audio-visual corpus
enriched with slides, reveal that our proposed LCB-net outperforms general ASR
model by 9.4%/9.1%/10.9% relative WER/U-WER/B-WER reduction on test set, which
enjoys high unbiased and biased performance. Moreover, we also evaluate our
model on LibriSpeech corpus, leading to 23.8%/19.2%/35.4% relative
WER/U-WER/B-WER reduction over the ASR model.
