Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions. (10th May 2010)

• Record Type: Journal Article
• Authors: Kolossa, Dorothea; Fernandez Astudillo, Ramon; Hoffmann, Eugen; Orglmeister, Reinhold
• Other Names: Deng Li Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2010(2010)
• Publication Date: 2010-05-10
• Subjects: Sound -- Recording and reproducing -- Digital techniques -- Periodicals; Computer sound processing -- Periodicals; Computer sound processing; Sound -- Recording and reproducing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 620.2
• DOI: 10.1155/2010/651420
• Languages: English
• ISSNs: 1687-4714

Abstract

Abstract : 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.