可解释的机器学习揭示了听力值与正常音频图的听众中的语音在噪音中的识别之间的关系
Jithin Raj Balan1, Hansapani Rodrigo2, Udit Saxena3
1Department of Speech, Language and Hearing Sciences, The University of Texas at Austin, Austin, Texas 78712, USA.
The Journal of the Acoustical Society of America
|October 12, 2023
概括
机器学习模型,特别是XGBoost,可以预测正常听力个体的语音噪音识别. 扩展的高频听力和年龄是关键预测因素,突出了它们对听觉感知的重要性.
科学领域:
- 听力学 听力学是指听力学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 一些有正常听力图的人在杂的环境中难以理解言语.
- 这种现象表明,标准听力测试可能无法完全捕捉与现实听力相关的听觉功能的所有方面.
研究的目的:
- 评估听力值对临床正常听力图的个体在噪音语音识别上的预测能力.
- 将各种机器学习模型 (GAM,ANN,DNN,RF,XGBoost) 的性能与标准统计模型进行比较.
- 在预测语音识别值 (SRT) 中确定特定听力测量频率和人口因素的相对重要性.
主要方法:
- 应用机器学习模型包括通用添加模型 (GAM),人工神经网络 (ANN),深度神经网络 (DNN),随机森林 (RF) 和极端梯度增强 (XGBoost).
- 利用了764名参与者的档案数据 (1528只耳朵) 进行正常的听力图,包括从0.25到16kHz的听力值和SRTs.
- 采用夏普利添加式解释 (SHAP) 来确定不同变量对SRT预测的贡献.
主要成果:
- 在机器学习模型中,XGBoost表现最好,平均绝对误差 (MAE) 为1.62dB.
- GAM实现了1.61dB的可比MAE,而ANN和RF显示了类似的结果 (MAE ≈1.67-1.68dB).
- SHAP分析确定了在延长高频 (16 kHz,12.5 kHz) 上的年龄和听力值作为SRT的显著预测因素.
结论:
- 机器学习模型,特别是XGBoost,可以有效地预测正常音频图的个体的语音噪音识别能力.
- 在扩展的高频范围内 (8kHz以上) 的听觉灵敏度在噪音中的语音感知中起着至关重要的作用,即使使用正常的常规听力图.
- 这些发现强调需要考虑扩展的高频听力和人口因素,以全面评估听觉功能.
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