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Publication
INTERSPEECH 2007
Conference paper
Trainable speaker diarization
Abstract
This paper presents a novel framework for speaker diarization. We explicitly model intra-speaker inter-segment variability using a speaker-labeled training corpus and use this modeling to assess the speaker similarity between speech segments. Modeling is done by embedding segments into a segment-space using kernel-PCA, followed by explicit modeling of speaker variability in the segment-space. Our framework leads to a significant improvement in diarization accuracy. Finally, we present a similar method for bandwidth classification.