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2018 | OriginalPaper | Buchkapitel

Gated Convolutional LSTM for Speech Commands Recognition

verfasst von : Dong Wang, Shaohe Lv, Xiaodong Wang, Xinye Lin

Erschienen in: Computational Science – ICCS 2018

Verlag: Springer International Publishing

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Abstract

As the mobile device gaining increasing popularity, Acoustic Speech Recognition on it is becoming a leading application. Unfortunately, the limited battery and computational resources on a mobile device highly restrict the potential of Speech Recognition systems, most of which have to resort to a remote server for better performance. To improve the performance of local Speech Recognition, we propose C-1-G-2-Blstm. This model shares Convolutional Neural Network’s ability of learning local feature and Recurrent Neural Network’s ability of learning sequence data’s long dependence. Furthermore, by adopting the Gated Convolutional Neural Network instead of a traditional CNN, we manage to greatly improve the model’s capacity. Our tests demonstrate that C-1-G-2-Blstm can achieve a high accuracy at 90.6% on the Google Speech Commands data set, which is 6.4% higher than the state-of-art methods.

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Vorheriges Kapitel Calibration and Monitoring of IoT Devices by Means of Embedded Scientific Visualization Tools

Nächstes Kapitel Enabling Machine Learning on Resource Constrained Devices by Source Code Generation of the Learned Models

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Titel: Gated Convolutional LSTM for Speech Commands Recognition
verfasst von: Dong Wang
Shaohe Lv
Xiaodong Wang
Xinye Lin
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2018
Print ISBN: 978-3-319-93700-7

Electronic ISBN: 978-3-319-93701-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93701-4_53

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