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        <title>Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion</title>
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    <name type="personal">
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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 text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding 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.</abstract>
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%0 Conference Proceedings
%T Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
%A Kim, Suyoun
%A Dalmia, Siddharth
%A Metze, Florian
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kim-etal-2019-gated
%X 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 text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding 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.
%R 10.18653/v1/P19-1107
%U https://aclanthology.org/P19-1107
%U https://doi.org/10.18653/v1/P19-1107
%P 1131-1141

Markdown (Informal)

[Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion](https://aclanthology.org/P19-1107) (Kim et al., ACL 2019)

• Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion (Kim et al., ACL 2019)

ACL

• Suyoun Kim, Siddharth Dalmia, and Florian Metze. 2019. Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1131–1141, Florence, Italy. Association for Computational Linguistics.