Improving ASR Contextual Biasing with Guided Attention
Jiyang Tang Kwangyoun Kim Suwon Shon Felix Wu Prashant Sridhar Shinji Watanabe
In this paper, we propose a Guided Attention (GA) auxiliary training loss,
which improves the effectiveness and robustness of automatic speech recognition
(ASR) contextual biasing without introducing additional parameters. A common
challenge in previous literature is that the word error rate (WER) reduction
brought by contextual biasing diminishes as the number of bias phrases
increases. To address this challenge, we employ a GA loss as an additional
training objective besides the Transducer loss. The proposed GA loss aims to
teach the cross attention how to align bias phrases with text tokens or audio
frames. Compared to studies with similar motivations, the proposed loss
operates directly on the cross attention weights and is easier to implement.
Through extensive experiments based on Conformer Transducer with Contextual
Adapter, we demonstrate that the proposed method not only leads to a lower WER
but also retains its effectiveness as the number of bias phrases increases.
Specifically, the GA loss decreases the WER of rare vocabularies by up to 19.2%
on LibriSpeech compared to the contextual biasing baseline, and up to 49.3%
compared to a vanilla Transducer.