PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers

End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunci...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Pandey, Rahul, Ren, Roger, Luo, Qi, Liu, Jing, Rastrow, Ariya, Gandhe, Ankur, Filimonov, Denis, Grant Strimel, Stolcke, Andreas, Bulyko, Ivan
Format: Article
Language:English
Subjects:
Adapters
Automatic speech recognition
Customization
Embedding
Transducers
Voice recognition
Words (language)
Online Access:Get full text
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Summary:End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunciation lexicon can be useful. We propose a PROnunCiation-aware conTextual adaptER (PROCTER) that dynamically injects lexicon knowledge into an RNN-T model by adding a phonemic embedding along with a textual embedding. The experimental results show that the proposed PROCTER architecture outperforms the baseline RNN-T model by improving the word error rate (WER) by 44% and 57% when measured on personalized entities and personalized rare entities, respectively, while increasing the model size (number of trainable parameters) by only 1%. Furthermore, when evaluated in a zero-shot setting to recognize personalized device names, we observe 7% WER improvement with PROCTER, as compared to only 1% WER improvement with text-only contextual attention
ISSN:2331-8422
DOI:10.48550/arxiv.2303.17131