Unsupervised methods for speaker diarization
Name
756462731-MIT.pdf
Description
Full printable version
Size
11.83 MB
Format
Adobe PDF
Checksum (MD5)
8afaf79b85658da2d476eee3e487c896
Author(s)
Shum, Stephen (Stephen Hin-Chung)
Advisor(s)
James R. Glass and Najim Dehak.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
Given a stream of unlabeled audio data, speaker diarization is the process of determining "who spoke when." We propose a novel approach to solving this problem by taking advantage of the effectiveness of factor analysis as a front-end for extracting speaker-specific features and exploiting the inherent variabilities in the data through the use of unsupervised methods. Upon initial evaluation, our system achieves state-of-the art results of 0.9% Diarization Error Rate in the diarization of two-speaker telephone conversations. The approach is then generalized to the problem of K-speaker diarization, for which we take measures to address issues of data sparsity and experiment with the use of the von Mises-Fisher distribution for clustering on a unit hypersphere. Our extended system performs competitively on the diarization of conversations involving two or more speakers. Finally, we present promising initial results obtained from applying variational inference on our front-end speaker representation to estimate the unknown number of speakers in a given utterance.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-95).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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