多发性硬化症患者的语音抑郁情绪检测：跨语料库与跨语言研究
Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study
AI总结：
本研究探索语音AI在多发性硬化症（MS）患者中检测抑郁情绪的潜力，通过跨语料库和跨语言分析，结合传统语音特征、SER情感维度及探索性特征，模型在二分类任务中UAR达66%，特征选择后提升至74%，为并发疾病情境下的抑郁检测提供初步依据。
摘要：
抑郁症常与多发性硬化症（MS）等神经退行性疾病并发，但基于语音的人工智能在此类情境下检测抑郁症的潜力尚未被探索。本研究通过跨语料库和跨语言分析，利用普通人群的英文数据与多发性硬化症患者（pwMS）的德文数据，考察了基于语音的抑郁症检测方法对多发性硬化症患者的迁移性。我们的方法使用监督机器学习模型，采用以下三类特征：1）该领域常用的传统语音和语言特征；2）从语音情感识别（SER）模型中提取的情感维度；3）探索性语音特征分析。尽管数据有限，我们的模型在多发性硬化症患者的抑郁情绪检测中展现出中等泛化能力，在二分类任务中实现了66%的未加权平均召回率（UAR）。特征选择进一步提升了性能，将UAR提高至74%。我们的研究结果还强调，情绪变化在普通人群和多发性硬化症患者中均是抑郁情绪的相关指标。本研究为基于语音的抑郁症检测在存在并发疾病（如神经退行性疾病）时的泛化性提供了初步探索。
英文摘要：
Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in such contexts remains unexplored. This study examines the transferability of speech-based depression detection methods to people with MS (pwMS) through cross-corpus and cross-lingual analysis using English data from the general population and German data from pwMS. Our approach implements supervised machine learning models using: 1) conventional speech and language features commonly used in the field, 2) emotional dimensions derived from a Speech Emotion Recognition (SER) model, and 3) exploratory speech feature analysis. Despite limited data, our models detect depressive mood in pwMS with moderate generalisability, achieving a 66% Unweighted Average Recall (UAR) on a binary task. Feature selection further improved performance, boosting UAR to 74%. Our findings also highlight the relevant role emotional changes have as an indicator of depressive mood in both the general population and within PwMS. This study provides an initial exploration into generalising speech-based depression detection, even in the presence of co-occurring conditions, such as neurodegenerative diseases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
