NDLI: A Speech Endpoint Detection Based on Dynamically Updated Threshold of Box-Counting Dimension

Content Provider	IEEE Xplore Digital Library
Author	Gao Hongbin Pang Weiyi Huang Chunru Zhang Yongqiang
Copyright Year	2009
Abstract	Accurate endpoint detection is important for speech procession. The endpoint detection problem is nontrivial for non-stationary backgrounds where noises may be introduced by the speaker, the recording environment and the transmission system. In this paper, an effective endpoint detection algorithm is proposed for improving speech signal processing performance in noisy environment. The proposed speech/pause discrimination method is based on the box-counting dimension. At the same time, a dynamically updated threshold and adaptive window are used to improve the performance of the algorithm. It is characterized by higher accuracy or flexibility, faster processing speed and less computation. Through a large number of samples for laboratory experiments, the results show that the improvements in detection accuracy over representative endpoint detection algorithms for robust speech processing
Starting Page	397
Ending Page	401
File Size	1007108
Page Count	5
File Format	PDF
ISBN	9780769536002
DOI	10.1109/IFITA.2009.381
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	adaptive window signal processing Adaptive signal detection Fractals Information technology box-counting dimension Working environment noise Acoustic noise Signal processing algorithms Speech recognition speech endpoint detection Speech processing Detection algorithms Signal to noise ratio
Content Type	Text
Resource Type	Article

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