Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition

The Gaussian Mixture Model (GMM) is often used in conjunction with Mel-frequency cepstral coefficient (MFCC) feature vectors for speaker recognition. A great challenge is to use these techniques in situations where only small sets of training and evaluation data are available, which typically results in poor statistical estimates and, finally, recognition scores. Based on the observation of marginal MFCC probability densities, we suggest to greatly reduce the number of free parameters in the GMM by modeling the single dimensions separately after proper preprocessing. Saving about 90% of the free parameters as compared to an already optimized GMM and thus making the estimates more stable, this approach considerably improves recognition accuracy over the baseline as the utterances get shorter and saves a huge amount of computing time both in training and evaluation, enabling real-time performance. The approach is easy to implement and to combine with other short-utterance approaches, and applicable to other features as well.