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Application of dynamic time warping optimization algorithm in speech recognition of machine translation
Shaohua Jiang1,2, Zheng Chen3
1School of Humanities, Fujian University of Technology, Fuzhou 350118, China.
Heliyon
|November 29, 2023
Summary
This study enhances speech recognition using the Dynamic Time Warping (DTW) algorithm, achieving high accuracy rates above 93% in quiet and 91% in noisy environments. The DTW algorithm also significantly improves recognition speed for speech signal processing.
Area of Science:
- Speech signal processing
- Human-computer interaction
Background:
- Speech recognition is crucial for human-computer interaction but faces challenges like low accuracy and speed.
- Existing methods suffer from interference and performance limitations.
Purpose of the Study:
- To investigate and improve speech recognition accuracy and speed.
- To evaluate the effectiveness of the Dynamic Time Warping (DTW) algorithm in speech recognition.
Main Methods:
- Speech data was converted into sequences using an acoustic model.
- The Dynamic Time Warping (DTW) algorithm was applied for speech preprocessing (sampling, windowing).
- Speech feature extraction was performed before recognition.
Main Results:
- High recognition rates were achieved: above 93.85% in quiet environments and 91.4% in noisy environments.
- Average recognition rates reached 95.8% (quiet) and 93% (noisy) across 10 testers.
- DTW-based speech recognition demonstrated a fast vocabulary recognition speed.
Conclusions:
- The Dynamic Time Warping (DTW) algorithm significantly enhances speech recognition performance.
- DTW offers a robust solution for accurate and rapid speech recognition, even in challenging acoustic conditions.
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