用语音检测多发性硬化患者抑郁情绪，跨语种数据验证有效
Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study
- 结合语音特征与情绪识别模型，构建跨语种抑郁检测方法
- 在多发性硬化患者中实现66%的平均召回率，特征筛选后达74%
- 首次证明语音可作为神经退行性疾病患者抑郁的可靠指标
抑郁症常与多发性硬化（MS）等神经退行性疾病共病，但基于语音的人工智能在该人群中的抑郁检测潜力尚未探索。本研究通过跨语种、跨数据集分析，利用来自普通人群的英语数据和多发性硬化患者（pwMS）的德语数据，检验语音抑郁检测方法的可迁移性。采用监督学习模型，融合三类特征：1）领域常用语音与语言特征；2）从语音情绪识别（SER）模型提取的情绪维度；3）探索性语音特征分析。尽管数据有限，模型在二分类任务中对多发性硬化患者的抑郁情绪检测达到66%的未加权平均召回率（UAR），经特征选择后提升至74%。研究还表明情绪变化在普通人群及多发性硬化患者中均为抑郁的重要指标。该工作首次探索了在共病神经退行性疾病背景下，语音基抑郁检测的泛化能力。
原文摘要 · Abstract (English)
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.
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