Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition

Tianyi Xu, Zhanheng Yang, Kaixun Huang, Pengcheng Guo, Ao Zhang, Biao Li, Changru Chen, Chao Li, Lei Xie

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

By incorporating additional contextual information, deep biasing methods have emerged as a promising solution for speech recognition of personalized words. However, for real-world voice assistants, always biasing on such personalized words with high prediction scores can significantly degrade the performance of recognizing common words. To address this issue, we propose an adaptive contextual biasing method based on Context-Aware Transformer Transducer (CATT) that utilizes the biased encoder and predictor embeddings to perform streaming prediction of contextual phrase occurrences. Such prediction is then used to dynamically switch the bias list on and off, enabling the model to adapt to both personalized and common scenarios. Experiments on Librispeech and internal voice assistant datasets show that our approach can achieve up to 6.7% and 20.7% relative reduction in WER and CER compared to the baseline respectively, mitigating up to 96.7% and 84.9% of the relative WER and CER increase for common cases. Furthermore, our approach has a minimal performance impact in personalized scenarios while maintaining a streaming inference pipeline with negligible RTF increase.

Original languageEnglish
Pages (from-to)1668-1672
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2023-August
DOIs
StatePublished - 2023
Event24th International Speech Communication Association, Interspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023

Keywords

  • Context-Aware Training
  • Contextual List Filtering
  • End-to-end Speech Recognition
  • RNN-T

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