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Key technologies of pre-processing and post-processing methods for embedded automatic speech recognition systems

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4 Author(s)
Dongzhi He ; Inst. of Embedded Software & Syst., Beijing Univ. of Technol., Beijing, China ; Yibin Hou ; Yuanyuan Li ; Zhi-Hao Ding

Signal pre-processing and post-processing are becoming two key factors that impact embedded speech recognition systems from the laboratory to practical application. Speech endpoint detection and out-of-vocabulary rejection are the most important part of the speech pre-processing and post-processing respectively. The performance of traditional speech endpoint detection based on short-term energy and zero-crossing rate degrade dramatically in noisy environments. Methods based on frequency-domain need complex computing, and they can not meet embedded systems well. In this paper, we present a new endpoint detection algorithm that is based on statistical theory for isolated-word. The correct endpoint detection rate reaches 97.40% using the method. In this paper one-class support vector machine theory is introduced to solve out-of-vocabulary rejection. Using this algorithm system, true recognition fraction(TRF) is up to 96%, and false recognition fraction(FRF ) is about 95%.

Published in:

Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on

Date of Conference:

15-17 July 2010

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