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Speaker identification using time-delay HMEs

K Chen1, D Xie, H Chi

  • 1National Lab of Machine Perception, Peking University, Beijing, China.

International Journal of Neural Systems
|March 1, 1996
PubMed
Summary
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This study enhances Hierarchical Mixture of Experts (HME) for temporal processing in speaker identification. The novel approach achieves good performance and significantly faster training for speaker recognition systems.

Area of Science:

  • Machine Learning
  • Signal Processing
  • Biometrics

Background:

  • Speaker identification is crucial for security and authentication.
  • Existing methods face challenges with temporal data and training stability.
  • Hierarchical Mixture of Experts (HME) offers a framework for complex classification tasks.

Purpose of the Study:

  • To extend Hierarchical Mixture of Experts (HME) for temporal data processing.
  • To improve text-dependent speaker identification accuracy and training efficiency.
  • To address training instability issues in multiway classification.

Main Methods:

  • Developed a time-delay Hierarchical Mixture of Experts (HME) for spatio-temporal processing.
  • Proposed a generalized Bernoulli density to replace multinomial logit density for stability.

Related Experiment Videos

  • Introduced a combining scheme for multiple time-delay HMEs for multi-scale analysis.

    • Main Results:

    • The proposed time-delay HME with EM algorithm demonstrated good performance in speaker identification.
    • The system achieved significantly faster training times compared to existing methods.
    • Effectively addressed issues related to time-delay techniques within the HME framework.

    Conclusions:

    • The extended HME model is effective for temporal processing in speaker identification.
    • The generalized Bernoulli density improves training stability.
    • The multi-scale analysis using combined HMEs enhances system robustness and efficiency.