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Maximum entropy PLDA for robust speaker recognition under speech coding distortion

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Abstract

The system combining i-vector and probabilistic linear discriminant analysis (PLDA) has been applied with great success in the speaker recognition task. The i-vector space gives a low-dimensional representation of a speech segment and training data of a PLDA model, which offers greater robustness under different conditions. In this paper, we propose a new framework based on i-vector/PLDA and Maximum Entropy (ME) to improve the performance of speaker identification system in the presence of speech coding distortion. The results are reported on TIMIT database and speech coding obtained by passing the speech test from TIMIT database through the AMR encoder/decoder. Our results show that the proposed methode achieves improved performance when compared with the i-vector/PLDA and MEGMM.

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Authors and Affiliations

  1. Speech Communication and Signal Processing Laboratory Université des Sciences et de la Technologie Houari Boumediene (USTHB), Algiers, Algeria

    Ahmed Krobba & Mohamed Debyeche

  2. LARIHS Laboratory, Campus Shappaing, University of Moncton, Moncton, Canada

    Sid. Ahmed Selouani

Authors
  1. Ahmed Krobba
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  2. Mohamed Debyeche
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  3. Sid. Ahmed Selouani
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Correspondence to Ahmed Krobba.

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Krobba, A., Debyeche, M. & Selouani, S.A. Maximum entropy PLDA for robust speaker recognition under speech coding distortion. Int J Speech Technol 22, 1115–1122 (2019). https://doi.org/10.1007/s10772-019-09642-5

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  • DOI: https://doi.org/10.1007/s10772-019-09642-5

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