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Text dependant speaker recognition using MFCC, LPC and DWT

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Abstract

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.

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Authors and Affiliations

  1. Speech Communication and Signal Processing Laboratory, Faculty of Electronics Engineering and Computer Science, University of Science and Technology Houari Boumedienne (USTHB), Box no: 32 El Alia, 16111, Algiers, Algeria

    Fatma Zohra Chelali & Amar Djeradi

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  1. Fatma Zohra Chelali
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  2. Amar Djeradi
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Correspondence to Fatma Zohra Chelali.

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Chelali, F.Z., Djeradi, A. Text dependant speaker recognition using MFCC, LPC and DWT. Int J Speech Technol 20, 725–740 (2017). https://doi.org/10.1007/s10772-017-9441-1

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