Significance of the MUSIC-group delay method in an ICA-Beamforming framework for speech separation in multi source environments

The performance of an ICA-Beamforming framework in multi source environments is often limited by the resolution of the direction of arrival (DOA) estimation and by permutation errors. In this paper a framework that addresses these issues, using the MUSIC-Group delay method of DOA estimation has been described. A new cost function defined for this purpose iteratively computes the correlation between the signals recovered using ICA and beamforming methods with signals recovered from the MUSIC-Group delay method as a reference. This cost function is then used to select the demixing matrix at each iteration until a convergence criterion is met. Source separation is then carried out using the final demixing matrix. Since the MUSIC-Group delay method exhibits high resolution, the DOA estimates obtained can be sorted more effectively to solve the permutation problems in ICA. TIMIT speech data is spatialized under a reverberant environment at various direct-to-reverberant energy ratio (DRR) to obtain S-TIMIT data. Experiments on speaker dependent large vocabulary speech recognition are conducted for a mixture of two speakers from the S-TIMIT data. The word error rates corresponding to the target and the non-target speaker using the proposed method indicate reasonable improvements when compared to conventional methods like ICA and ICA-Beamforming methods.