Personalization for BERT-based Discriminative Speech Recognition Rescoring
share
Recognition of personalized content remains a challenge in end-to-end speech recognition. We explore three novel approaches that use personalized content in a neural rescoring step to improve recognition: gazetteers, prompting, and a cross-attention based encoder-decoder model. We use internal de-identified en-US data from interactions with a virtual voice assistant supplemented with personalized named entities to compare these approaches. On a test set with personalized named entities, we show that each of these approaches improves word error rate by over 10 that on this test set, natural language prompts can improve word error rate by 7 gazetteers were found to perform the best with a 10 rate (WER), while also improving WER on a general test set by 1
READ FULL TEXT