A Neural Model for Contextual Biasing Score Learning and Filtering
This work addresses the challenge of enhancing ASR accuracy for users by filtering candidate phrases, though it is incremental as it builds on existing biasing methods.
The paper tackles the problem of improving automatic speech recognition (ASR) by integrating external knowledge through contextual biasing, resulting in significant accuracy improvements under various biasing conditions on the Librispeech benchmark.
Contextual biasing improves automatic speech recognition (ASR) by integrating external knowledge, such as user-specific phrases or entities, during decoding. In this work, we use an attention-based biasing decoder to produce scores for candidate phrases based on acoustic information extracted by an ASR encoder, which can be used to filter out unlikely phrases and to calculate bonus for shallow-fusion biasing. We introduce a per-token discriminative objective that encourages higher scores for ground-truth phrases while suppressing distractors. Experiments on the Librispeech biasing benchmark show that our method effectively filters out majority of the candidate phrases, and significantly improves recognition accuracy under different biasing conditions when the scores are used in shallow fusion biasing. Our approach is modular and can be used with any ASR system, and the filtering mechanism can potentially boost performance of other biasing methods.