Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions

When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA) has proven a valuable tool for this...

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Bibliographic Details
Published in:EURASIP journal on audio, speech, and music processing speech, and music processing, 2010-01, Vol.2010 (1), p.1-13
Main Authors: Kolossa, Dorothea, Fernandez Astudillo, Ramon, Hoffmann, Eugen, Orglmeister, Reinhold
Format: Article
Language:English
Subjects:
Acoustics
Engineering
Engineering Acoustics
Independent component analysis
Mathematical analysis
Mathematics in Music
Nonlinearity
Recognition
Research Article
Robustness
Signal,Image and Speech Processing
Speech
Vectors (mathematics)
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Summary:When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA) has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques.
ISSN:1687-4714
1687-4722
DOI:10.1155/2010/651420