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The speech features used for speaker recognition should uniquely reflect characteristics of the speaker's vocal tract apparatus and contain negligible information about the linguistic contents in the speech. Cepstral features such as Linear Predictive Spectral Coefficients (LPCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are most commonly used features for speaker recognition task, but found...
The widespread use of automatic speaker recognition technology in real world applications demands for robustness against various realistic conditions. In this paper, a robust spectral feature set, called NDSF (Normalized Dynamic Spectral Features) is proposed for automatic speaker recognition in mismatch condition. Magnitude spectral subtraction is performed on spectral features for compensation against...
Speaker recognition in mismatched condition is a vital issue in the recent years as training and testing speech data can be distorted due various practical conditions. Filter bank based cepstral features are used in many speech, speaker and language recognition tasks. These filter banks are mainly designed according to auditory based principles of speech processing, with variation in shape of filters...
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