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Related Experiment Videos

A modular and hybrid connectionist system for speaker identification

Y Bennani1

  • 1C.N.R.S., L.I.P.N. URA-1507, University of Paris-Nord, Villetaneuse, France.

Neural Computation
|July 1, 1995
PubMed
Summary

This study introduces a modular/hybrid system for speaker identification, combining connectionist and Hidden Markov Model modules. The novel approach achieved perfect speaker identification in tests, demonstrating its effectiveness for complex tasks with limited data.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Speech Processing

Background:

  • Speaker identification is complex, especially with limited training data.
  • Modularity in connectionist systems aids in managing complexity and incorporating prior knowledge.
  • Text-independent speaker identification presents unique challenges.

Purpose of the Study:

  • To present and evaluate a modular/hybrid connectionist system for speaker identification.
  • To test the efficacy of modularity in complex, data-limited scenarios.
  • To develop an architecture integrating multiple connectionist modules with a Hidden Markov Model.

Main Methods:

  • Developed a hybrid architecture combining several connectionist modules.
  • Integrated a Hidden Markov Model module into the system.

Related Experiment Videos

  • Evaluated the system on the DARPA-TIMIT database with 102 speakers.

    • Main Results:

    • Achieved perfect speaker identification in tests.
    • Demonstrated the effectiveness of the modular/hybrid approach.
    • Validated the system's performance on a substantial speaker population.

    Conclusions:

    • The modular/hybrid connectionist system is highly effective for speaker identification.
    • Modularity is a valuable technique for complex AI tasks like speaker recognition.
    • The proposed architecture offers a robust solution for text-independent speaker identification.