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English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm.

Jin Zhang1

  • 1School of Foreign Languages, Xinyang Agriculture and Forestry University, Xinyang 464000, Henan, China.

Computational Intelligence and Neuroscience
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved speech recognition system to help Chinese students enhance English pronunciation. The new model provides accurate feedback, improving learning outcomes compared to traditional methods.

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Area of Science:

  • Computational Linguistics
  • Educational Technology
  • Speech Processing

Background:

  • Growing internationalization necessitates improved English proficiency in China.
  • Current computer-aided language learning systems lack comprehensive pronunciation evaluation.
  • Traditional speech recognition struggles with English pronunciation nuances and accuracy.

Purpose of the Study:

  • To develop an advanced speech recognition system for accurate English pronunciation assessment.
  • To address limitations in existing computer-aided language learning tools for oral training.
  • To enhance the English pronunciation learning experience for Chinese students.

Main Methods:

  • Developed a nonlinear network structure simulating the human brain for speech analysis.
  • Utilized Mel frequency cepstral coefficients and a deep belief network.
  • Improved traditional computer pronunciation evaluation methods.

Main Results:

  • The proposed system provides learners with accurate pronunciation quality analysis and guidance.
  • The system effectively corrects intonation and improves the overall learning effect.
  • Experimental data show the improved system's recognition ability surpasses traditional models.

Conclusions:

  • The enhanced speech recognition system offers a superior solution for English pronunciation training.
  • This approach significantly benefits Chinese learners by providing targeted feedback.
  • The study validates the effectiveness of the novel speech recognition model for educational purposes.