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This paper develops a system to automatically distinguish natural speech from synthetic speech. The issue of feature selection is considered. We take commonly used feature Mel-Frequency Cepstrum Coefficient (MFCC) in consideration, as well as other features such as Relative Phase Shift (RPS) and pitch tuned for Automatically Speech Recognition (ASR). We found some features are complimentary in the...
This paper reports an approach to language identification (LID) system fusion using discriminative training. Maximum mutual information (MMI) training for Gaussian mixture model is introduced to the standard LDA-GMM fusion framework. Experimental results show that the proposed fusion scheme outperforms the maximum likelihood (ML) trained backend of LID system. The impact of number of Gaussian mixtures...
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