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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Combining Articulatory Features with End-to-End Learning in Speech Recognition

verfasst von : Leyuan Qu, Cornelius Weber, Egor Lakomkin, Johannes Twiefel, Stefan Wermter

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

End-to-end neural networks have shown promising results on large vocabulary continuous speech recognition (LVCSR) systems. However, it is challenging to integrate domain knowledge into such systems. Specifically, articulatory features (AFs) which are inspired by the human speech production mechanism can help in speech recognition. This paper presents two approaches to incorporate domain knowledge into end-to-end training: (a) fine-tuning networks which reuse hidden layer representations of AF extractors as input for ASR tasks; (b) progressive networks which combine articulatory knowledge by lateral connections from AF extractors. We evaluate the proposed approaches on the speech Wall Street Journal corpus and test on the eval92 standard evaluation dataset. Results show that both fine-tuning and progressive networks can integrate articulatory information into end-to-end learning and outperform previous systems.

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Metadaten
Titel
Combining Articulatory Features with End-to-End Learning in Speech Recognition
verfasst von
Leyuan Qu
Cornelius Weber
Egor Lakomkin
Johannes Twiefel
Stefan Wermter
Copyright-Jahr
2018
Buch
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01423-0

Electronic ISBN: 978-3-030-01424-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01424-7_49

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