Abstract
In today’s telecommunications environment, which includes wireless, landline, VoIP, and computer networks, the mismatch between training and testing environments poses a big challenge to speaker authentication systems. In Chapter 8, we addressed the mismatch problem from a feature extraction point of view. In this chapter, we address the problem from an acoustic modeling point of view. These two approaches can be used independently or jointly.
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References
Atal, B. S.: “Automatic recognition of speakers from their voices”. Proceeding of the IEEE 64, 460–475 (1976)
Atal, B. S.: “Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification”. Journal of the Acoustical Society of America 55, 1304–1312 (1974)
Furui, S.: “Cepstral analysis techniques for automatic speaker verification”. IEEE Trans. Acoust., Speech, Signal Processing 27, 254–277 (1981)
Li, Q., Parthasarathy, S., Rosenberg, A. E.: “A fast algorithm for stochastic matching with application to robust speaker verification,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Munich), pp. 1543–1547, April 1997
Mammone, R. J., Zhang, X., Pamachandran, R. P.: “Robust speaker recognition”. IEEE Signal Processing Magazine 13, 58–71 (1996)
Mansour, D., Juang, B.-H.: “A family of distortion measures based upon projection operation for robust speech recognition”. IEEE Trans. Acoust., Speech, Signal Processing 37, 1659–1671 (1989)
Parthasarathy, S., Rosenberg, A. E.: “General phrase speaker verification using sub-word background models and likelihood-ratio scoring,” in Proceedings of ICSLP-96 (Philadelphia), October 1996
Rahim, M. G., Juang, B.-H.: “Signal bias removal by maximum likelihood estimation for robust telephone speech recognition”. IEEE Transactions on Speech and Audio Processing 4, 19–30 (1996)
Rosenberg, A. E., Lee, C.-H., Soong, F. K.: “Cepstral channel normalization techniques for HMM-based speaker verification,” in Proceedings of Int. Conf. on Spoken Language Processing (Yokohama, Japan), pp. 1835–1838, 1994
Sankar, A., Lee, C.-H.: “A maximum-likelihood approach to stochastic matching for robust speech recognition” IEEE Transactions on Speech and Audio Processing 4, 190–202 (1996)
Surendran, A. C.: Maximum-likelihood stochastic matching approach to non-linear equalization for robust speech recognition. PhD thesis, Rutgers University, Busch, NJ, May 1996
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© 2012 Springer-Verlag Berlin Heidelberg
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Li, Q.(. (2012). Robust Speaker Verification with Stochastic Matching. In: Speaker Authentication. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23731-7_10
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DOI: https://doi.org/10.1007/978-3-642-23731-7_10
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