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

Suyoun Kim, Siddharth Dalmia, Florian Metze


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.
Anthology ID:
P19-1107
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1131–1141
Language:
URL:
https://www.aclweb.org/anthology/P19-1107
DOI:
10.18653/v1/P19-1107
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1107.pdf