Speaker Segmentation and Clustering Using Gender Information
GENERAL DYNAMICS ADVANCED INFORMATION SYSTEMS DAYTON OH
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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.
- Voice Communications