Text dependant speaker recognition using MFCC, LPC and DWT

Text dependant speaker recognition using MFCC, LPC and DWT The objective of this work is to investigate the benefit of discrete wavelet transform combined with LPC, for speaker identification system applied for Algerian Berber language, compared to the traditional Mel frequency analysis. We’ve developed a speaker identification system for Algerian Berber language. The corpus concerns two dataset, the first one concerns eight isolated words and the second is dedicated for continuous speech repeated by Algerian native Berber. We’ve used MFCC feature, their first and second derivatives and discrete wavelet transform (DWT) followed by linear predictive coding (LPC) to ameliorate the parameterization phase. Mahalanobis distance, ascendant classification and pitch analysis were used for characterizing our speech signals. We evaluate the performance of DWT–LPC feature for clean and additive noisy speech. The multilayer perceptron classifier was used for this purpose, efficiency was improved for DWT combined with LPC feature vectors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Speech Technology Springer Journals

Text dependant speaker recognition using MFCC, LPC and DWT

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Springer US
Copyright © 2017 by Springer Science+Business Media, LLC
Engineering; Signal,Image and Speech Processing; Social Sciences, general; Artificial Intelligence (incl. Robotics)
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