Catalog-based single-channel speech-music separation for automatic speech recognition

In this study, we analyze the effect of the catalog-based single-channel speech-music separation method, which we proposed previously, on speech recognition performance. In the proposed method, assuming that we know a catalog of the background music, we developed a generative model for the superposed speech and music spectrograms. We represent the speech spectrogram by a Non-negative Matrix Factorization (NMF) model and the music spectrogram by a conditional Poisson Mixture Model (PMM). In this paper, we propose to recover the speech signals from the mixed signal in time-domain by detecting the active catalog frames using the catalog-based method. We compare the performances of 3 different signal reconstruction techniques; Expectation-Based, Posterior-Based and Time-Domain reconstruction. Moreover, we compare the performance of our system with the performance of the traditional NMF model. Our method outperforms the NMF method in ASR performance and separation performance in most experimental conditions.