Blind Spatial Subtraction Array with Independent Component Analysis for Hands-free Speech Recognition

In this paper, we propose a new blind spatial subtraction array (BSSA) which contains an accurate noise estimator based on independent component analysis (ICA) to realize a noise-robust hands-free speech recognition. First, a preliminary experiment suggests that the conventional ICA is proficient in the noise estimation rather than the direct speech estimation in real environments, where the target speech can be approximated to a point source but real noises are often not point sources. Secondly, based on the above-mentioned findings, we propose a new noise reduction method which is implemented in subtracting the power spectrum of the estimated noise by ICA from the power spectrum of noise-contaminated observations. This architecture provides us with a noise-estimation-error robust speech enhancement which is well applicable to the speech recognition. Finally, the effectiveness of the proposed BSSA is shown in the speech recognition experiment.