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使用GRU-Mixer架构与Log-Mel谱图特征的语言疼痛水平分类

Adi Alhudhaif¹

¹Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 27, 2025

概括

本研究介绍了Gated Recurrent Unit (GRU) -Mixer,这是一个用于自动检测语音疼痛的深度学习模型. 它在分类疼痛水平方面达到很高的准确性,为非侵入性患者评估提供了一个有前途的工具.

科学领域:

计算语言学计算语言学
情感计算是一种情感计算.
机器学习用于医疗保健

背景情况:

语音自动疼痛检测提供非侵入性,实时评估,对于无法自我报告的患者至关重要.
现有的方法需要进一步开发,以获得强大的临床应用.

研究的目的:

介绍和评估Gated Recurrent Unit (GRU) -Mixer,这是一个基于语音的疼痛分类的新型深度学习模型.
为未来有关情感计算和疼痛识别的研究建立一个基准.

主要方法:

开发了一种轻量级的重复深度学习模型 (GRU-Mixer),从语音中处理Log-Mel光谱图.
该模型使用堆叠的双向GRU和适应平均聚合来提取时间特征.
使用类平衡损失的扬声器独立训练用于对二进制,分级强度和热状态疼痛分类任务的概括.

主要成果:

GRU-Mixer在二元疼痛检测 (疼痛与非疼痛) 中获得了83.86%的准确性.
多类疼痛强度分类 (轻度,中度,严重) 达到75.36%的准确性.
该模型在TAME疼痛数据集上表现出强的表现.

结论:

GRU-Mixer为基于语音的疼痛识别提供了一个有效的基准架构.
这项研究是TAME Pain数据集上的第一个深度学习分类工作.
这些发现支持AI在客观疼痛评估中通过声乐生物标志物的潜力.

关键词:

这是一个GRU-Mixer.罗格-梅尔光谱图罗格-梅尔光谱图疼痛水平的确定疼痛水平的确定语言信号语音信号语音信号

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