A non-negative approach to language informed speech separation

Gautham J. Mysore, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of high level information in source separation algorithms can greatly constrain the problem and lead to improved results by limiting the solution space to semantically plausible results. The automatic speech recognition community has shown that the use of high level information in the form of language models is crucial to obtaining high quality recognition results. In this paper, we apply language models in the context of speech separation. Specifically, we use language models to constrain the recently proposed non-negative factorial hidden Markov model. We compare the proposed method to non-negative spectrogram factorization using standard source separation metrics and show improved results in all metrics.

Original languageEnglish (US)
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages356-363
Number of pages8
DOIs
StatePublished - 2012
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: Mar 12 2012Mar 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Country/TerritoryIsrael
CityTel Aviv
Period3/12/123/15/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'A non-negative approach to language informed speech separation'. Together they form a unique fingerprint.

Cite this