Speech Separation and Speaker Recognition-Review
Mamta Sood, Krishna Gopal Soni, Monika Cheema · 2013
Abstract — Speech separation and its recognition is based on two different phenomenons, first is speech separation and second is speech recognition. The speech separation is based on the time-domain which is depends on the full unconstrained decomposition of the speech sample just because in constrained approach it become practically hard to compute and also limits the performance of the system. The decomposition is done by an appropriate independent component analysis (ICA) algorithm giving independent components that are grouped into clusters corresponding to the original sources. Speech recognition (SR) is aimed to recognize the speech in large population. And in large population is very time-consuming and impose a bottleneck. So for fast recognition we use GMM based k-mean algorithm for fast recognition of speech. For speeding up the whole process the clustered signals are used. Then during the test stage only a small proportion of speaker models in selected clusters are used in the likelihood computations resulting in a significant speed-up with little to no loss in accuracy. Keywords- Speech separation, Speech recognition (SR),