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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. [ABSTRACT FROM AUTHOR]

This paper deals with the underlining and the prominent display of the compensatory strategies developed by six speakers which are from different geographic regions when they express themselves in a second language very different from their mother native language. The paradigm that had been used in this study is the elocution speed as a speaking constraint in order to investigate the effects of the flow rate on the acoustical parameters of the speech signal. The analysis is performed in both time and frequency domains. We have obtained very interesting results as we have found out that the used constraint allows a good discrimination between different speakers on the basis of their native language. [ABSTRACT FROM AUTHOR]

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