Speaker Segmentation and Clustering using Gender Information

Abstract

This paper considers the segmentation and clustering of conversational speech for the two-wire training (3conv2w) and two-wire testing (1conv2w) conditions of the NIST 2005 speaker recognition evaluation. A notable feature of the system described is that each file is labeled as containing either opposite- or same-gender speakers. The speech segments for opposite-gender files are clustered by gender, while those for same-gender files are processed by agglomerative clustering. By using gender information in the clustering of the opposite-gender files, the equal error rate in the 3conv2w training condition was reduced from 15.2% to 9.9%. For the 1conv2w testing condition, clustering opposite-gender files by gender did not improve performance over agglomerative clustering; however, it was over 100 times faster than agglomerative clustering on the opposite-gender files