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Endpoint Detection of Speech Signal Using Lyapunov Exponent  

Zang, Xian (Control and Instrumentation Department, Chonbuk National University)
Kim, Jeong-Yeon (Control and Instrumentation Department, Chonbuk National University)
Chong, Kil-To (Electronics and Information Department, Chonbuk National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.46, no.1, 2009 , pp. 28-33 More about this Journal
Abstract
In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. The conventional methods for speech endpoint detection are based on two simple time-domain measurements-short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Accordingly, this algorithm is low complexity and suitable for Digital Isolated Word Recognition System.
Keywords
Digital Isolated Word Recognition; Time-domain; Time-dependent Lyapunov exponent;
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1 Zebulum, R.S.; Vellasco, M.; Perelmuter, G.; Pacheco, M.A.; 'A COMPARISON OF DIFFERENT SPECTRAL ANALYSIS MODELS FOR SPEECH RECOGNITION USING NEURAL NETWORKS', IEEE 39th Midwest symposium on Circuits and Systems, 1996., Volume 3, 18-21 Aug. 1996 Page(s):1428 - 1431 vol.3   DOI
2 Kokkinos, I.; Maragos, P., 'Nonlinear speech analysis using models for chaotic systems', Speech and Audio Processing, IEEE, volume 13, Issue 6, Nov. 2005 Page(s): 1098-1109   DOI   ScienceOn
3 Steven B. Davis and Paul Mermelstein, 'Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences', IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-28, No.4, August 1980   DOI
4 Adriano. Petry, D. A. C. Barone, 'Preliminary experiments in speaker verification using time-dependent largest Lyapunov expontent', Computer Speech and Language, 17 (2003), 403-413   DOI   ScienceOn
5 H. Sakoe, S. Chiba, 'Dynamic programming algorithm optimization for spoken work recognition', IEEE Trans. ASSP 26(1978) 43-49   DOI
6 H. Silverman, D. Morgan, 'The application of dynamic programming to connected speech segmentation', IEEE ASSP Mag. 7, no.3(1990) 7-25   DOI   ScienceOn
7 M. De Wachter, M. Matton, K. Demuynck, P.Wambacq, R. Cools, D. Van Comernolle, 'Template-based Continuous Speech Recognition', IEEE Trans. ASLP 15 (2007) 1377-1390   DOI   ScienceOn
8 L. R. Rabiner and M. R. Sambur, 'An Algorithm for Determining the Endpoints of Isolated Utterances', Bell Syst. Tech. J., vol. 54, No. 2, pp. 297-315, February 1975   ScienceOn
9 M. R. Sambur and L. R. Rabiner, 'A Speaker Independent Digit-Recognition System', Bell Syst. Tech. J., vol. 54, No. 1, pp. 81-102, January 1975   DOI   ScienceOn
10 F. Itakura, 'Minimum prediction residual principle applied to speech recognition', IEEE Trans. ASSP 23 (1975) 67-72   DOI
11 Joseph W. Picone, 'Signal Modeling Techniques in Speech Recognition', Proceeding of the IEEE, vol.81, No.9, pages 1215-1247, 1993   DOI   ScienceOn