Convolutive Blind Source Separation with Wiener Post-Filtering for Robust Speech Recognition

DI PERSIA, L. E.; MILONE, D. H.; YANAGIDA, M.

Mendoza

Simposio; 7th Argentine Symposium on Computing Technology; 2006

SADIO

Blind source separation for convolutive mixtures of sound sources is a complex task, mainly because the mixing filters are long and non-minimum phase. One approach to solve this problem is frequency domain blind source separation, in which the separation is calculated for each frequency bin in the time-frequency domain. Although there are several methods for this task, separation quality is degraded by many factors. This paper presents a method for separation in time-frequency domain, that combines the advantages of other two separation methods and uses a time-frequency Wiener filter as post-processing to increase separation quality. The algorithm has been evaluated over a database of Spanish speech recorded in a reverberant room using two active sound sources and two microphones. Speech recognition results show an increment in recognition rate of the separated speech in the order of 70% from the noisy case.