Signal modeling for speaker identification

A large number of parameters, including pitch, LPCC, ΔLPCC, PARCOR, MFCC, ΔMFCC, and residual cepstrum (RCEP) were extracted from speech signals and their effectiveness for text-independent speaker identification was evaluated. In addition, the usefulness of two signal processing techniques, preemphasis and cepstral weighting, was also studied. The VQ-based speaker recognition method with codebooks fine-tuned by LVQ algorithm was used. It was shown that both LPCC and MFCC are effective representations, for smaller number of parameters, LPCC representation performs better but is surpassed by MFCC if the analysis order is larger. Pitch is an independent parameter so that it can be used jointly with other spectral features. In an evaluation experiment, the correct identification rate for 112 male speakers with test utterances of less than one second reached 98.2%