Unsupervised speaker segmentation with residual phase and MFCC features

This paper proposes an unsupervised method for improving the automatic speaker segmentation performance by combining the evidence from residual phase (RP) and mel frequency cepstral coefficients (MFCC), This method demonstrates the complementary nature of speaker specific information present in the residual phase in comparison with the information present in the conventional MFCC. Moreover this method presents an unsupervised speaker segmentation algorithm based on support vector machine (SVM). The experiments show that the combination of residual phase and MFCC helps to identify more robustly the transitions among speakers. (C) 2009 Elsevier Ltd. All rights reserved.