ESPNet: End-to-end speech processing toolkit

Shinji Watanabe; Takaaki Hori; Shigeki Karita; Tomoki Hayashi; Jiro Nishitoba; Yuya Unno; Nelson Enrique Yalta Soplin; Jahn Heymann; Matthew Wiesner; Nanxin Chen; Adithya Renduchintala; Tsubasa Ochiai

doi:10.21437/Interspeech.2018-1456

ESPNet: End-to-end speech processing toolkit

Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai

Research output: Contribution to journal › Conference article › peer-review

878 Citations (Scopus)

Abstract

This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

Original language	English
Pages (from-to)	2207-2211
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	2018-September
DOIs	https://doi.org/10.21437/Interspeech.2018-1456
Publication status	Published - 2018
Externally published	Yes
Event	19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India Duration: 2018 Sept 2 → 2018 Sept 6

Keywords

Dynamical neural network
End-to-end
Kaldi
Open source software
Speech recognition

ASJC Scopus subject areas

Language and Linguistics
Human-Computer Interaction
Signal Processing
Software
Modelling and Simulation

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10.21437/Interspeech.2018-1456

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Watanabe, S., Hori, T., Karita, S., Hayashi, T., Nishitoba, J., Unno, Y., Soplin, N. E. Y., Heymann, J., Wiesner, M., Chen, N., Renduchintala, A., & Ochiai, T. (2018). ESPNet: End-to-end speech processing toolkit. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018-September, 2207-2211. https://doi.org/10.21437/Interspeech.2018-1456

Watanabe, S, Hori, T, Karita, S, Hayashi, T, Nishitoba, J, Unno, Y, Soplin, NEY, Heymann, J, Wiesner, M, Chen, N, Renduchintala, A & Ochiai, T 2018, 'ESPNet: End-to-end speech processing toolkit', Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2018-September, pp. 2207-2211. https://doi.org/10.21437/Interspeech.2018-1456

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title = "ESPNet: End-to-end speech processing toolkit",

abstract = "This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.",

keywords = "Dynamical neural network, End-to-end, Kaldi, Open source software, Speech recognition",

author = "Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Soplin, {Nelson Enrique Yalta} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai",

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year = "2018",

doi = "10.21437/Interspeech.2018-1456",

language = "English",

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AU - Watanabe, Shinji

AU - Hori, Takaaki

AU - Karita, Shigeki

AU - Hayashi, Tomoki

AU - Nishitoba, Jiro

AU - Unno, Yuya

AU - Soplin, Nelson Enrique Yalta

AU - Heymann, Jahn

AU - Wiesner, Matthew

AU - Chen, Nanxin

AU - Renduchintala, Adithya

AU - Ochiai, Tsubasa

PY - 2018

Y1 - 2018

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AB - This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

KW - Dynamical neural network

KW - End-to-end

KW - Kaldi

KW - Open source software

KW - Speech recognition

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SN - 2308-457X

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JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Y2 - 2 September 2018 through 6 September 2018

ER -

ESPNet: End-to-end speech processing toolkit

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Keywords

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