Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition

The introduced entity encoder enables the entity list to be By incorporating additional contextual information, deep biasing personalized for individual users. However, this personalization methods have emerged as a promising solution for speech comes at a cost: the model has less prior knowledge of the customized recognition of personalized words. However, for real-world words, which can result in false alarms. In other words, voice assistants, always biasing on such personalized words the model may mistakenly identify non-entity names as entity with high prediction scores can significantly degrade the performance terms, leading to a decrease in overall recognition performance, of recognizing common words. To address this issue, particularly for words that are phonemically similar. For example, we propose an adaptive contextual biasing method based if we add "José" as a context phrase, the ASR system on Context-Aware Transformer Transducer (CATT) that utilizes might falsely recognize "O say can you see" as "José can you the biased encoder and predictor embeddings to perform see". This issue is particularly acute for a general ASR system streaming prediction of contextual phrase occurrences. Such that is not restricted to a particular domain. As a result, this prediction is then used to dynamically switch the bias list on and drawback makes biased models less competitive, as the benefits off, enabling the model to adapt to both personalized and common gained may be outweighed by the negative impact on overall scenarios.