NDLI: A robust algorithm for detecting speech segments using an entropic contrast

Content Provider	IEEE Xplore Digital Library
Author	Waheed, K. Weaver, K. Salam, F.M.
Copyright Year	2002
Description	Author affiliation: Circuits, Syst. & Artificial Neural Networks Lab., Michigan State Univ., East Lansing, MI, USA (Waheed, K.; Weaver, K.; Salam, F.M.)
Abstract	This paper addresses the issue of automatic word/sentence boundary detection in both quiet and noisy environments. We propose to use an entropy based contrast function between the speech segments and the background noise. A simplified data based scheme of computing the entropy of the speech data is presented. The entropy-based contrast exhibits better-behaved characteristics as compared to the energy-based methods. An adaptive threshold is used to determine the candidate speech segments, which are subjected to word/sentence constraints. Experimental. results show that this algorithm outperforms energy-based algorithms. The improved detection accuracy of speech segments results in at least 25% improvement of recognition performance for isolated speech and more than 16% for connected speech. For continuous speech, a preprocessing stage comprising of the proposed speech segment detection makes the overall HMM based scheme more computationally efficient by rejection of silence periods.
Sponsorship	IEEE Circuits & Syst. Soc. School of Electr. & Comput. Eng. at Oklahoma State Univ
File Size	274152
File Format	PDF
ISBN	0780375238
DOI	10.1109/MWSCAS.2002.1187039
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Entropy Working environment noise Speech enhancement Speech recognition Automatic speech recognition Engines Signal to noise ratio Background noise Detection algorithms
Content Type	Text
Resource Type	Article

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