Abstract
This paper investigates how window size and shift period affect the performance of a speaker verification system. Specifically, we investigate their effects on verification accuracy and computation time of speaker verification systems built using the mel-warped cepstral feature extraction and Guassian Mixture Model. Experiments show that window size should not be larger than a critical point, which is determined by testing with a set of registered speakers. Otherwise, the computation time increases while the verification accuracy decreases.
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Leung, C., Moon, Y. (2003). Effect of Window Size and Shift Period in Mel-Warped Cepstral Feature Extraction on GMM-Based Speaker Verification. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_52
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DOI: https://doi.org/10.1007/3-540-44887-X_52
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