A Novel Low Complexity VQ-Based Single Channel Speech Separation Technique

In this paper, a new single channel speech separation technique based on vector quantization (VQ) and the MIXMAX approximation is presented. At the core of this approach are two trained codebooks of the quantized feature vectors of speakers, whereby the main evaluation for separation is performed. The performance of the VQ-based approach is evaluated by applying three separate features: log spectrum, modulated lapped transform (MLT) coefficients, and a fusion of pitch and envelop information. The experiments are conducted in two different scenarios: speaker-dependent and speaker independent. The results show that the log spectrum outperforms the other features for speaker-dependent scenario. However, for the speaker-independent scenario, the best results are obtained from applying the pitch-envelop feature