Abstract
In this paper, a speaker segmentation method based on log-likelihood ratio score (LLRS) over universal background model (UBM) and a speaker clustering method based on difference of log-likelihood scores between two speaker models are proposed. During the segmentation process, the LLRS between two adjacent speech segments over UBM is used as a distance measure Cwhile during the clustering process Cthe difference of log-likelihood scores between two speaker models is used as a speaker classification criterion. A complete system for NIST 2002 2-speaker task is presented using the methods mentioned above. Experimental results on NIST 2002 Switchboard Cellular speaker segmentation corpus, 1-speaker evaluation corpus and 2- speaker evaluation corpus show the potentiality of the proposed algorithms.
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© 2006 Springer-Verlag Berlin Heidelberg
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Deng, J., Zheng, T.F., Wu, W. (2006). UBM Based Speaker Segmentation and Clustering for 2-Speaker Detection. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_16
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DOI: https://doi.org/10.1007/11939993_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
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