Using dynamic conditional random field on single-microphone speech separation

The use of dynamic conditional random field (DCRF) for model-based single-microphone speech separation is investigated. The speech sources are represented by acoustic state sequences from speaker-dependent acoustic models. The posterior probabilities of the source acoustic states given a speech mixture are inferred with a maximum entropy probability distribution which is represented by DCRF. The posterior probabilities are needed for minimum mean-square error estimation of the speech sources. Loopy belief propagation is applied for the inference. Averaged stochastic gradient descent and limited-memory BFGS are compared for parameter estimation. With the log-magnitude spectrum of the speech mixture as input observation, the proposed method achieves better separation performance in terms of Blind Source Separation Metrics (SDR, SAR, SIR) and PESQ than a factorial hidden Markov model baseline system in our experiments.