Conference Paper
Dynamic segmental vector quantization in isolatedword speech recognition
Dept. of Comput. Eng., Kyung Hee Univ., YonginSi, South Korea
DOI: 10.1109/ISSPIT.2004.1433722 Conference: Signal Processing and Information Technology, 2004. Proceedings of the Fourth IEEE International Symposium on Source: IEEE Xplore
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Conference Paper: New cepstral representation using wavelet analysis and spectral transformation for robust speech recognition.
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ABSTRACT: The goal is to improve the speech recognition rate by optimisation of mel frequency cepstral coefficients (MFCCs): modifications concern the timefrequency representations used to estimate these coefficients. There are many ways to obtain a spectrum out of a signal which differ in the method itself (Fourier, wavelets,...), and in the normalisation. We show that we can obtain noise resistant cepstral coefficients, for speaker independent connected word recognition. The recognition system is based on a continuous whole word hidden Markov model. An error reduction rate of approximately 50% is achieved with word modelsThe 4th International Conference on Spoken Language Processing, Philadelphia, PA, USA, October 36, 1996; 01/1996  01/1993; Prentice Hall., ISBN: 9780130151575
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ABSTRACT: In this paper, we discuss the use of weighted filter bank analysis (WFBA) to increase the discriminating ability of mel frequency cepstral coefficients (MFCCs). The WFBA emphasizes the peak structure of the log filter bank energies (LFBEs) obtained from filter bank analysis while attenuating the components with lower energy in a simple, direct, and effective way. Experimental results for recognition of continuous Mandarin telephone speech indicate that the WFBAbased cepstral features are more robust than those derived by employing the standard filter bank analysis and some widely used cepstral liftering and frequency filtering schemes both in channeldistorted and noisy conditions.IEEE Signal Processing Letters 04/2001; · 1.67 Impact Factor
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