Improving Automatic Speech Recognition with Neural Embeddings

Christopher Li
2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, 111 8th Ave New York, NY 10011(2021)
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A common challenge in automatic speech recognition (ASR) systems is successfully decoding utterances containing long tail entities. Examples of entities include unique contact names and local restaurant names that may be out of vocabulary, and therefore absent from the training set. As a result, during decoding, such entities are assigned low likelihoods by the model and are unlikely to be recognized. In this paper, we apply retrieval in an embedding space to recover such entities. In the aforementioned embedding space, embedding representations of phonetically similar entities are designed to be close to one another in cosine distance. We describe the neural networks and the infrastructure to produce such embeddings. We also demonstrate that using neural embeddings improves ASR quality by achieving an over 50% reduction in word error rate (WER) on evaluation sets for popular media queries.