Improving Speaker Identification Rate Using Fractals

This paper reports on a text-dependent speaker identification system that combines Mel-frequency cepstral coefficients with non-linear turbulence information extracted using multi-scale fractal dimension (MFD). The MFD is estimated using Box-Counting and Minkowiski-Bouligand dimension. The proposed framework is implemented in conjunction with sub-band based speaker identification system. Results show that the proposed framework with Box-Counting feature extraction improves the performance of the classical wideband approach by up to 10% identification rate. It is further observed that the proposed framework gives the improved Bhattacharyya distance between impostors and speakers´ speech distributions.