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Related Experiment Video

Updated: May 29, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Learning and plan refinement in a knowledge-based system for automatic speech recognition.

R De Mori1, L Lam, M Gilloux

  • 1School of Computer Science, McGill University, Montreal, P. Q. H3A 2K6, Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

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This study presents a semiautomatic speech recognition system design using planning and inductive learning. Performance data refines the system, achieving successful connected letter recognition in experiments.

Area of Science:

  • Artificial Intelligence
  • Speech Technology
  • Machine Learning

Background:

  • Designing speech recognition systems traditionally involves complex, manual processes.
  • Integrating planning and learning offers a more adaptable approach to system development.

Purpose of the Study:

  • To demonstrate a semiautomatic design methodology for speech recognition systems.
  • To leverage recognition performance for system refinement and inductive learning for parameter optimization.

Main Methods:

  • A planning-based framework was employed for system design.
  • Recognition performance metrics guided the plan refinement process.
  • Inductive learning algorithms were utilized to set action preconditions.

Main Results:

Related Experiment Videos

Last Updated: May 29, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

  • The semiautomatic design approach was successfully implemented.
  • Experimental results demonstrated effective recognition of connected letters.
  • The system was tested with data from 100 distinct speakers.

Conclusions:

  • Semiautomatic design, integrating planning and inductive learning, is a viable method for speech recognition systems.
  • Performance-driven refinement and inductive learning enhance system adaptability and accuracy.