动态时间扭曲优化算法的应用在机器翻译语音识别中的应用
Shaohua Jiang1,2, Zheng Chen3
1School of Humanities, Fujian University of Technology, Fuzhou 350118, China.
Heliyon
|November 29, 2023
概括
这项研究使用动态时间扭曲 (DTW) 算法增强了语音识别,在安静环境中达到93%以上的高准确率,在噪音环境中达到91%. DTW算法还显著提高了语音信号处理的识别速度.
科学领域:
- 语音信号处理 语音信号处理
- 人与计算机的互动.
背景情况:
- 语音识别对于人机交互至关重要,但面临着低准确度和速度等挑战.
- 现有的方法存在干扰和性能限制.
研究的目的:
- 为了调查和提高语音识别的准确性和速度.
- 评估动态时间扭曲 (DTW) 算法在语音识别中的有效性.
主要方法:
- 使用声学模型将语音数据转换为序列.
- 动态时间扭曲 (DTW) 算法用于语音预处理 (采样,窗口).
- 在识别之前进行了语音特征提取.
主要成果:
- 识别率很高:在安静的环境中超过93.85%,在杂的环境中超过91.4%.
- 在10个测试器中,平均识别率达到95.8% (安静) 和93% (杂).
- 基于DTW的语音识别显示了快速的词汇识别速度.
结论:
- 动态时间扭曲 (DTW) 算法显著提高了语音识别性能.
- DTW提供了一个强大的解决方案,用于准确和快速的语音识别,即使在具有挑战性的声学条件下.
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