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arXiv · 1906.11604

Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion

Suyoun Kim·Siddharth Dalmia·Florian Metze
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Abstract

We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use the text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding a significant improvement in word error rate with better conversational-context representation. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.

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Suyoun Kim, Siddharth Dalmia, Florian Metze. 2019-06-27. Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion. https://arxiv.org/abs/1906.11604

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