使用机器学习自动评估语音质量
Yat Chun Au1, Nan Yan2, Manwa L Ng1
1Speech Science Laboratory, Faculty of Education, University of Hong Kong, Hong Kong, China.
Logopedics, phoniatrics, vocology
|February 25, 2026
概括
机器学习模型使用声学语音分析准确地预测失声症的严重程度. 渐变增强算法,特别是LightGBM,显示近乎专家的协议,增强客观的临床语音评估.
科学领域:
- 语音和语言病理学 语言病理学
- 计算语言学 计算语言学
- 生物医学工程 生物医学工程
背景情况:
- 临床语音评估依赖于对失声症严重性的主观感知评级.
- 现有的方法缺乏客观性,可重现性和效率.
- 自动声学分析为标准化语音评估提供了潜力.
研究的目的:
- 开发和验证机器学习模型,用于自动预测失声症严重程度 (GRBAS尺度的等级参数).
- 提高临床语音评估的客观性,可重现性和效率.
- 为了确定可预测感知失声症严重程度的关键声学特征.
主要方法:
- 从三个数据库收集了524个持续的语音样本.
- 使用Parselmouth (Praat) 提取了47个声学特征 (光谱, cepstral,扰动,基于噪声).
- 使用5倍交叉验证训练和评估了五个机器学习分类器 (DT,RF,XGBoost,LightGBM,CatBoost).
主要成果:
- 渐变增强算法 (LightGBM,CatBoost,XGBoost) 的表现优于传统的基于树的模型.
- 轻GBM实现了最高的平方加权卡帕 (QWK) 0.945.
- 首斯特拉测量 (CPPS,CSID,AVQI) 和HNR是评级的最有影响力的预测指标,而和闪的贡献最小.
结论:
- 梯度增强方法,特别是LightGBM,显示出与感知性听障评级的近乎专家一致.
- 这些模型为临床语音评估提供客观,可解释的工具.
- 自动预测失声症严重程度可以改善临床工作流程和诊断一致性.
关键词:
失声症 (Dysphonia) 是一种听力障碍.格拉巴斯 (GRBAS) 是一个粗的植物.轻GBMM 轻GBM 轻GBM 轻GBM路易斯·耶稣 路易斯·耶稣声学分析 声学分析机器学习是机器学习.更多相关视频
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