Abstract
A novel text-independent verification system based on the fractional Brownian motion (M_dim_fBm) for automatic speaker recognition (ASR) is presented in this paper. The performance of the proposed M_dim_fBm was compared to those achieved with the GMM (Gaussian Mixture Models) classifier using the mel-cepstral coefficients. We have used a speech database – obtained from fixed and cellular phones – uttered by 75 different speakers. The results have shown the superior performance of the M_dim_fBm classifier in terms of recognition accuracy. In addition, the proposed classifier employs a much simpler modeling structure as compared to the GMM.
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© 2004 Springer-Verlag Berlin Heidelberg
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Ana, R.S., Coelho, R., Alcaim, A. (2004). A New Classifier for Speaker Verification Based on the Fractional Brownian Motion Process. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_32
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DOI: https://doi.org/10.1007/978-3-540-30120-2_32
Publisher Name: Springer, Berlin, Heidelberg
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