Structured mean field method for single-microphone speech separation with factorial Hidden Markov Model

A variational statistical inference method referred as structured mean field method is studied for factorial Hidden Markov Model (HMM) formulation of single-microphone speech separation problem. By decoupling the Markov chains of the individual speech sources coupled during the mixing process of the speech mixture, the complexity of temporal inference of the speech sources is reduced to quadratic with the number of acoustic states of the sources. Speech separation and automatic speech recognition experiments are performed on the reconstructed speech. Experimental results show that the studied approximating inference method achieves the similar separation results as the exact inference algorithm in terms of Perceptual Evaluation of Speech Quality (PESQ) and word error rate (WER).