NAM+: Towards Scalable End-to-End Contextual Biasing for Adaptive ASR
Tsendsuren Munkhdalai, Zelin Wu, Golan Pundak, Khe Chai Sim, Jiayang Li, Pat Rondon, Tara N. Sainath
Abstract
Attention-based biasing techniques for end-to-end ASR systems are able to achieve large accuracy gains without requiring the inference algorithm adjustments and parameter tuning common to fusion approaches. However, it is challenging to simultaneously scale up attention-based biasing to realistic numbers of biased phrases; maintain in-domain WER gains, while minimizing out-of-domain losses; and run in real time. We present NAM+, an attention-based biasing approach which achieves a 16X inference speedup per acoustic frame over prior work when run with 3,000 biasing entities, as measured on a typical mobile CPU. NAM+ achieves these run-time gains through a combination of Two-Pass Hierarchical Attention and Dilated Context Update. Compared to the adapted baseline, NAM+ further decreases the in-domain WER by up to 12.6% relative, while incurring an out-of-domain WER regression of 20% relative. Compared to the non-adapted baseline, the out-of-domain WER regression is 7.1 % relative.