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深度学习和人工智能应用于在帕金森病中模拟语音和语言

Daniel Escobar-Grisales¹, Cristian David Ríos-Urrego¹, Juan Rafael Orozco-Arroyave^1,2

¹GITA Lab, Faculty of Engineering, University of Antioquia, Medellín 050010, Colombia.

Diagnostics (Basel, Switzerland)

|July 14, 2023

概括

语音分析,而不是语言,在检测帕金森病 (PD) 中显示出更高的准确性. 这项研究发现,语音生物标志物更好地区分PD患者和健康个体,优于基于语言的方法和多模式方法.

科学领域:

神经科学是一个神经科学.
计算语言学计算语言学
人工智能的人工智能

背景情况:

帕金森病 (PD) 是一种流行的神经退行性疾病,影响运动和非运动功能,包括言语和语言.
虽然研究了PD的语言障碍,但用于认知评估的基于语言的生物标志物仍未得到充分研究.
使用生物标志物的PD自动检测和监测是一个活跃的研究领域.

研究的目的:

建议和评估使用语音和语言生物标志物的帕金森病自动评估方法.
为了比较不同机器学习模型的有效性,包括CNN和预训练网络,用于PD分类.
调查语音和语言模式融合对分类准确性的影响.

主要方法:

使用了一维和二维卷积神经网络 (CNN).
雇佣了预先训练有素的模型:Wav2Vec 2.0用于语音和BERT/BETO用于语言.
研究了言语和语言的独立建模,随后是早期,联合和晚期的融合策略.

主要成果:

语音模式在将帕金森病患者与健康对照者 (HC) 分类时达到高达88%的准确性.
与语言表示和多模式方法相比,语音表示显示出更高的歧视性.
融合策略表明,语音模式中的潜在信息丢失与多模式表示时间跨度的变化有关,影响了准确性.

结论:

语音生物标志物比语言生物标志物更有效地将帕金森病患者与健康人区分开来.
需要进一步的研究来探索最佳的融合方法和时间跨度,用于PD检测中的多模式分析.
自动语音分析为发现和监测帕金森病提供了一个有希望的途径.

关键词:

帕金森病是帕金森氏症的一种疾病.在Wav2Vec中,使用Wav2Vec.卷积神经网络是一种卷积神经网络.自然语言处理自然语言处理.语音处理语音处理词嵌入词嵌入词嵌入

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