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Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework

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Abstract

The performance of speaker recognition system is highly dependent on the duration of speech used in enrollment and test. This work presents a detailed experimental review and analysis of the GMM-SVM based speaker recognition system in presence of duration variability. This article also reports a comparison of the performance of GMM-SVM classifier with its precursor technique Gaussian mixture model- universal background model (GMM-UBM) classifier in presence of duration variability. The goal of this research work is not to propose a new algorithm for improving speaker recognition performance in presence of duration variability. However, the main focus of this work is on utterance partitioning (UP), a commonly used strategy to compensate the duration variability issue. We have analysed in detailed the impact of training utterance partitioning in speaker recognition performance under GMM-SVM framework. We further investigate the reason why the utterance partitioning is important for boosting speaker recognition performance. We have also shown in which case the utterance partitioning could be useful and where not. Our study has revealed that utterance partitioning does not reduce the data imbalance problem of the GMM-SVM classifier as claimed in earlier study. Apart from these, we also discuss issues related to the impact of parameters such as number of Gaussians, supervector length, amount of splitting required for obtaining better performance in short and long duration test conditions from speech duration perspective. We have performed the experiments with telephone speech from POLYCOST corpus consisting of 130 speakers.

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Acknowledgements

The authors are grateful to Professor Goutam Saha, Department of E & ECE, IIT Kharagpur for his help in the experimentation with the POLYCOST database. First author is extremely grateful to Dr. Richa Mittal, erstwhile student of Department of CET, IIT Kharagpur for her help at the time of preparation of the manuscript. First author is also extremely grateful to Dr. Rahul Dasgupta, erstwhile student of Department of CET, IIT Kharagpur for rigorous technical discussions.

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Authors and Affiliations

  1. R. H. Sapat College of Engineering, Management Studies and Research, Nashik, 422005, India

    Nirmalya Sen

  2. CNRS, Inria, LORIA, Université de Lorraine, 54000, Nancy, France

    Md Sahidullah

  3. Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, 382 007, India

    Hemant A. Patil

  4. Centre for Educational Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Shyamal Kumar Das Mandal

  5. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Krothapalli Sreenivasa Rao

  6. B. P. Poddar Institute of Management and Technology, V.I.P Road, Kolkata, India

    Tapan Kumar Basu

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  1. Nirmalya Sen
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Correspondence to Md Sahidullah.

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Sen, N., Sahidullah, M., Patil, H.A. et al. Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework. Int J Speech Technol 24, 1067–1088 (2021). https://doi.org/10.1007/s10772-021-09862-8

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  • DOI: https://doi.org/10.1007/s10772-021-09862-8

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