Relative effectiveness of score normalization methods in speaker identification fusing acoustic and prosodic information

In this paper, an investigation on the fusion of acoustic and prosodic information for GMM-UBM based text-independent speaker identification is presented. When acoustic and prosodic based systems are established, it is advantageous to normalize the dynamic ranges of the score dimensions, that is, likelihood scores from different quality of acoustic- and prosodic-based models. Score normalization methods, linear scaling to unit range and linear scaling to unit variance, are applied to transform the output scores using the background instances so as to obtain meaningful comparison between speaker models. In this fusion system based on linear score weighting approach, the performance of speaker identification is further improved when incorporating prosodic level of information. Experimental results on part of the NIST 1999 SRE corpus are reported.