Multichannel NMF with Reduced Computational Complexity for Speech Recognition

In this study, we propose efficient the number of computational iteration method of MNMF for speech recognition. The proposed method initializes and estimates the MNMF algorithm with respect to the estimated spatial correlation matrix reducing the number of iteration of update algorithm. This time, mask emphasis via Expectation Maximization algorithm is used for estimation of a spatial correlation matrix. As another method, we propose a computational complexity reduction method via decimating update of the spatial correlation matrixH. The experimental result indicates that our method reduced the computational complexity of MNMF. It shows that the performance of the conventional MNMF was maintained and the computational complexity could be reduced.