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

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.