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《Chinese Journal of Engineering》 2015-09
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Continuous speech recognition by convolutional neural networks

ZHANG Qing-qing;LIU Yong;PAN Jie-lin;YAN Yong-hong;Key Laboratory of Speech Acoustics and Content Understanding,Chinese Academy of Sciences;  
Convolutional neural networks( CNNs),which show success in achieving translation invariance for many image processing tasks,were investigated for continuous speech recognition. Compared to deep neural networks( DNNs),which are proven to be successful in many speech recognition tasks nowadays,CNNs can reduce the neural network model sizes significantly,and at the same time achieve even a better recognition accuracy. Experiments on standard speech corpus TIMIT and conversational speech corpus show that CNNs outperform DNNs in terms of the accuracy and the generalization ability.
【Fund】: 国家自然科学基金资助项目(11161140319 91120001 61271426);; 中国科学院战略性先导科技专项(XDA06030100 XDA06030500);; 国家高技术研究发展计划资助项目(2012AA012503);; 中国科学院重点部署项目(KGZD-EW-103-2)
【CateGory Index】: TN912.34;TP183
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