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Speech separation using non-negative features and sparse non-negative matrix factorization

By Mikkel N. Schmidt

Abstract

This paper describes a method for separating two speakers in a single channel recording. The separation is performed in a low dimensional feature space optimized to represent speech. For each speaker, an overcomplete basis is estimated using sparse non-negative matrix factorization, and a mixture is separated by mapping the mixture onto the joint bases of the two speakers. The method is evaluated in terms of word recognition rate on the speech separation challenge data set. Key words: Speech separation challenge, Sparse non-negative matrix factorization

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.9337
Provided by: CiteSeerX
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