UBM Based Speaker Segmentation and Clustering for 2-Speaker Detection

摘要

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, while during the clustering process, the 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.