Phoebe: Pronunciation-aware contextualization for end-to-end speech recognition
Abstract
End-to-End (E2E) automatic speech recognition (ASR) systems learn word spellings directly from text-audio pairs, in contrast to traditional ASR systems which incorporate a separate pronunciation lexicon. The lexicon allows a traditional system to correctly spell rare words unobserved in training, if their phonetic pronunciation is known during inference. E2E systems, however, are more likely to misspell rare words.
In this work we propose an E2E model which benefits from the best of both worlds: it outputs graphemes, and thus learns to spell words directly, while also being able to leverage pronunciations for words which might be likely in a given context. Our model, which we name Phoebe, is based on the recently proposed Contextual Listen Attend and Spell model (CLAS). As in CLAS, our model accepts a set of bias phrases and learns an embedding for them which is jointly optimized with the rest of the ASR system. In contrast to CLAS, which accepts only the textual form of the bias phrases, the proposed model also has access to phonetic embeddings, which as we show improves performance on challenging test sets which include words unseen in training. The proposed model provides a 16% relative word error rate reduction over CLAS when both the phonetic and written representation of the context bias phrases are used.
In this work we propose an E2E model which benefits from the best of both worlds: it outputs graphemes, and thus learns to spell words directly, while also being able to leverage pronunciations for words which might be likely in a given context. Our model, which we name Phoebe, is based on the recently proposed Contextual Listen Attend and Spell model (CLAS). As in CLAS, our model accepts a set of bias phrases and learns an embedding for them which is jointly optimized with the rest of the ASR system. In contrast to CLAS, which accepts only the textual form of the bias phrases, the proposed model also has access to phonetic embeddings, which as we show improves performance on challenging test sets which include words unseen in training. The proposed model provides a 16% relative word error rate reduction over CLAS when both the phonetic and written representation of the context bias phrases are used.
Research Areas
