Yakun Hu

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In this paper, we address the speech-based gender identification problem. Mel-Frequency Cepstral Coefficients (MFCC) of voice samples are typically used as the features for gender identification. However, MFCC-based classification incurs high complexity. This paper proposes a novel pitch-based gender identification system with a two-stage classifier to(More)
—In this paper, we address the problem of large population speaker identification under noisy conditions. Major techniques for speaker identification is based on Mel-Frequency Cepstral Coefficients (MFCC), Gaussian Mixture Model (GMM) and Universal Background Model (UBM) which we call MFCC+GMM and MFCC+GMM+UBM. The approaches are known to perform very well(More)
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