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The Mel-frequency cepstral coefficient is the most widely used feature in speech and speaker recognition. However, the traditional MFCC is very sensitive to noise interference, which tends to drastically degrade the performance of recognition systems because of the mismatches between training and testing. In this paper, we proposed a new speaker recognition algorithm based on the dynamic MFCC parameters...
Studies have shown that depending on speaker task and environmental conditions, recognizers are sensitive to noisy stressful environments. The focus of this study is to achieve robust recognition in diverse environmental conditions through extracting robust features. Central to the technique is Root Cepstrum Coefficients (RCC) method, instead of logarithm amplitude spectrum and discrete cosine transform...
Mel-frequency cepstral coefficients (MFCC) are the most widely used features for speech recognition. However, MFCC-based speech recognition performance degrades in presence of additive noise. In this paper, we propose a set of noise-robust features based on conventional MFCC feature extraction method. Our proposed method consists of two steps. In the first step, mel sub-band Wiener filtering is carried...
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