Monaural Speech Separation Based on Gain Adapted Minimum Mean Square Error Estimation

We present a new model-based monaural speech separation technique for separating two speech signals from a single recording of their mixture. This work is an attempt to solve a fundamental limitation in current model-based monaural speech separation techniques in which it is assumed that the data used in the training and test phases of the separation model have the same energy level. To overcome this limitation, a gain adapted minimum mean square error estimator is derived which estimates sources under different signalto-signal ratios. Specifically, the speakers’ gains are incorporated as unknown parameters into the separation model and then the estimator is derived in terms of the source distributions and the signal-to-signal ratio. Experimental results show that the proposed system improves the separation performance significantly when compared with a similar model without gain adaptation as well as a maximum likelihood estimator with gain estimation. A preliminary version of this paper was presented at the IEEE Workshop on Machine Learning for Signal Processing (MLSP) held in Thessaloniki, Greece in August 2007. M. H. Radfar (B) · R. M. Dansereau Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada e-mail: [email protected] R. M. Dansereau e-mail: [email protected] M. H. Radfar · W.-Y. Chan Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada W.-Y. Chan e-mail: [email protected]