Spectral trajectory estimation using nonnegative matrix factorization for model-based monaural speech separation

This paper presents a study on model-based speech separation for monaural speech mixture. With prior knowledge about of the text content of the speech sources, we estimate the spectral envelope trajectory of each target source and use them to filter the mixture signal so that the target signal is enhanced and the interfering signal is suppressed. Accurate trajectory estimation is therefore crucial for successful separation. We proposed to use the nonnegative matrix factorization in the trajectory estimation process which improves the accuracy of the estimated trajectories considerably. Performance evaluation is carried out using mixtures of two equally-loud Cantonese speech sources. The proposed method is found to have significant improvement over previously proposed speech separation methods.