On the use of GSV-SVM for Speaker Diarization and Tracking

In this paper, we present the use of Gaussian Supervectors with Support Vector Machines classifiers (GSV-SVM) in an acoustic speaker diarization and a speaker tracking system, compared with a standard Gaussian Mixture Model system based on adapted Universal Background Models (GMM-UBM). GSVSVM systems (which share the adaptation step with the GMMUBM systems) are observed to have comparable performances: for acoustic speaker diarization, the GMM-UBM system outperforms the GSV-SVM system on ESTER2 data but the latter system works better in the speaker tracking system. In particular, the linear combination of two systems at the score level outperforms each individual system.