Evaluation of small-scale deep learning architectures in Thai speech recognition

Conference proceedings article

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Author list: Kaewprateep J., Prom-On S.

Publisher: Hindawi

Publication year: 2018

Start page: 60

End page: 64

Number of pages: 5

ISBN: 9781509052097

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049950212&doi=10.1109%2fECTI-NCON.2018.8378282&partnerID=40&md5=66c587956c3418005f69b3a25e297efa

Languages: English-Great Britain (EN-GB)

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Abstract

This paper presents a performance evaluation study for small-scale deep learning neural network for Thai speech recognition task. Convolutional neural network and long short term memory networks were built with a relatively small size dataset and small constructs. The aim of this study is to determine which method would be suitable for a small-scale deep learning study. Relatively small speech corpus was used to build deep-learning neural networks with two different architectures, including convolutional neural network (CNN) model and long short term memory (LSTM) model. Models were evaluated using cross validation technique and compare to one another. The result shows that CNN outperformed LSTM for a small-scale deep learning. This suggests that with the limited dataset and small-scale architecture CNN is a more suitable choice in the speech recognition study. ฉ 2018 IEEE.

Keywords

Long short term memory network, Thai speech recognition