Fractional Fourier transform based auditory feature for language identification

In this paper, a novel auditory feature based on fractional Fourier transform (FRFT), namely, fractional auditory cepstrum coefficient (FACC), is presented for language identification (LID). Different from the widely used Mel-frequency cepstrum coefficient (MFCC), the proposed feature utilizes the human auditory model and performs Gammatone filtering for the short-time fractional spectrum of the speech. Experimental results on NIST 2003 Language Recognition Evaluation (LRE03) show that the FACC feature decreases the equal error rate (EER) of 10.5% relatively when compared with the MFCC feature.

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