Speaker verification based on combining speaker individuality parameter selection and decision

We propose a new framework to incorporate speaker individuality parameters, such as pitch, vocal tract length and speaking rate, into designing speaker recognition systems. Based on our preliminary observations, a single pitch parameter may be more powerful than a vector of cepstral features for discriminating some speakers. Previous efforts have focused on concatenating these speaker parameters to existing MFCC based feature vectors. In this study a procedure is proposed to compare the effectiveness of the available set of parameters. The chosen parameter is then used to perform speaker verification. We test the proposed framework on the TIMIT database. Based on an intuitive parameter selection procedure to choose between a single pitch and the conventional 39-dim MFCC vector in a separate validation set, we found that parameter selection errors was reduced from 70, when only the MFCC parameter vector was used, to 25, when both parameter sets were made available in the selection process. For those 79 speakers whose corresponding pitch-based system was preferred for speaker verification, the average equal error rate was reduced from 23.1% to 18.4%. This strategy can be extended to incorporating other speaker individuality parameters