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KMID : 1022420170090020095
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Phonetics and Speech Sciences
2017 Volume.9 No. 2 p.95 ~ p.101
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DNN-based acoustic modeling for speech recognition of native and foreign speakers
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Kang Byung-Ok
Kwon Oh-Wook
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Abstract
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This paper proposes a new method to train Deep Neural Network (DNN)-based acoustic models for speech recognition of native and foreign speakers. The proposed method consists of determining multi-set state clusters with various acoustic properties, training a DNN-based acoustic model, and recognizing speech based on the model. In the proposed method, hidden nodes of DNN are shared, but output nodes are separated to accommodate different acoustic properties for native and foreign speech. In an English speech recognition task for speakers of Korean and English respectively, the proposed method is shown to slightly improve recognition accuracy compared to the conventional multi-condition training method.
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KEYWORD
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automatic speech recognition, Deep Neural Network (DNN), acoustic model
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