Distant Speaker Recognition Based on the Automatic Selection of Reverberant Environments Using GMMs

Channel distortion for a distant environment may drastically degrade the performance of speaker recognition because the training and test conditions differ significantly. In this paper, we propose robust distant speaker recognition that is based on the automatic selection of reverberant environments using Gaussian mixture models. Three methods involving (I) optimum channel determination, (II) joint optimum speaker and channel determination, or (III) optimum channel determination at the utterance level are proposed. Real-world speech data and simulated reverberant speech data are used to evaluate our proposed methods. The third proposed method achieves a relative error reduction of 69.6% over (baseline) speaker recognition using a reverberant environment-independent method, and it has performance equivalent to that of a reverberant environment-dependent method (an ideal-condition method).