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SPEAKER DIARIZATION BASED ON GMM SUPERVECTORS AND UNSUPERVISED INTRA-SPEAKER VARIABILITY MODELING

This paper presents a novel framework for speaker diarization. Audio is parameterized by a sequence of GMM-supervectors representing overlapping short segments of speech. Session dependent intra-session intra-speaker variability is estimated online in an unsupervised manner, and is removed from the supervectors using Nuisance Attribute Projection (NAP)� The supervectors are then projected using principal component analysis (PCA) into a one-dimensional space (for the two speaker case). Viterbi decoding is used to find an optimal segmentation using the projected supervectors as estimates for log-likelihood ratios. Finally, GMMs are adapted for each assumed speaker and Viterbi re-segmentation is used for producing a final diarization.