A Study on the Relation Between the Frame Pruning and the Robust Speaker Identification with Multivariate t-Distribution

In this paper, we performed the robust speaker identification based on the frame pruning and multivariate t-distribution respectively, and then studied on a theoretical basis for the frame pruning using the other methods. Based on the results from two methods, we showed that the robust algorithms based on the weight of frames become the theoretical basis of the frame pruning method by considering the correspondence between the weight of frame pruning and the conditional expectation of t-distribution. Both methods showed good performance when coping with the outliers occurring in a given time period, while the frame pruning method removing less reliable frames is recommended as one of good methods and, also, the multivariate t-distributions are generally used instead of Gaussian mixture models (GMM) as a robust approach for the speaker identification. In experiments, we found that the robust speaker identification has higher performance than the typical GMM algorithm. Moreover, we showed that the trend of frame likelihood using the frame pruning is similar to one of robust algorithms.