Monaural speaker segregation using group delay spectral matrix factorization

Abstract

Non-negative matrix factorization (NMF) methods have been widely used in single channel speaker separation. NMF methods use the magnitude of the Fourier transform for training the basis vectors. In this paper, a method that factorizes the spectral magnitude matrix obtained from the group delay function (GDF) is described. During training, pre-learning is applied on a training set of original sources. The bases are trained iteratively to minimize the approximation error. Separation of the mixed speech signal involves the factorization of the non negative group delay spectral matrix along with the use of fixed stacked bases computed during training. This matrix is then decomposed into a linear combination of trained bases for each individual speaker contained in the mixed speech signal. The estimated spectral magnitude for each speaker signal is modulated by the phase of mixed signal to reconstruct signal for each speaker signal. The separated speaker signals are further refined using a min-max masking method. Experiments on subjective evaluation, objective evaluation and multi speaker speech recognition on the TIMIT and the GRID corpus indicate reasonable improvements over other conventional methods.