Abstract
In speaker verification, a claimed speaker’s score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker’s and the impostors’ likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better score which can reduce that error. Experiments performed on the TI46 and the ANDOSL speech corpora show better results for the proposed method.
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Tran, D., Wagner, M. (2002). Fuzzy C-Means Clustering-Based Speaker Verification. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_42
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DOI: https://doi.org/10.1007/3-540-45631-7_42
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