Improving Speaker Identification Rate Using Fractals [IJCNN1005]
نویسندگان
چکیده
: 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.
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