PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers
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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 when measured on personalized entities and personalized rare entities, respectively, while increasing the model size (number of trainable parameters) by only 1 personalized device names, we observe 7 compared to only 1
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