Single channel dereverberation method in log-melspectral domain using limited stereo data for distant speaker identification

In this paper, we present a feature enhancement method that uses neural networks (NNs) to map the reverberant feature in a log-melspectral domain to its corresponding anechoic feature. The mapping is done by cascade NNs trained using Cascade 2 algorithm with an implementation of segment-based normalization. We assumed that the dimensions of feature were independent from each other and experimented on several assumptions of the room transfer function for each dimension. Speaker identification system was used to evaluate the method. Using limited stereo data, we could improve the identification rate for simulated and real datasets. On the simulated dataset, we could show that the proposed method is effective for both noiseless and noisy reverberant environments, with various noise and reverberation characteristics. On the real dataset, we could show that by using 6 independent NNs configuration for 24-dimensional feature and only 1 pair of utterances we could get 35% average error reduction relative to the baseline, which employed cepstral mean normalization (CMN).