基于情感刺激的语音抑郁症检测分析

Analysis of Speech Depression Detection Based on Emotional Stimuli

抑郁症是一种常见的精神障碍疾病,早期的检测和诊断对抑郁症预防和治疗至关重要。基于语音的抑郁症检测是当前计算机辅助检测方法中的一种高效、便捷的手段。为了探索不同的情感刺激是否对语音抑郁症检测存在影响,本文首先构建了抑郁症分析声学特征集,接着使用非参数检验的方法分析不同情感刺激性下抑郁与非抑郁个体之间声学特征的显著性差异,再采用情感刺激(积极、消极、中性)和任务类型(文本朗读、问答)组合的实验设计,通过机器学习和深度学习算法分别构建语音抑郁症检测模型。实验结果证明使用情感刺激会对抑郁症检测任务产生一定程度的影响,并且消极的情感刺激更容易诱发抑郁相关的情绪,对个体的发音特性产生影响,进而取得比积极刺激和中性语音更好的检测效果。

Depression is a common mental disorder,and early detection and diagnosis is essential for depression prevention and treatment.Speech depression detection is an efficient and convenient tool in the current computer-aided detection methods.In order to explore whether different emotional stimuli have an impact on speech depression detection,this paper firstly constructed an acoustic feature set for depression analysis,followed by analyzing the significant differences in acoustic features between depression and non-depression under different emotional stimuli using a non-parametric test,and then used an experimental design with a combination of emotional stimuli(positive,negative,neutral)and task types(text reading,question-and-answer)to build speech depression detection models through machine learning and deep learning algorithms.The experimental results demonstrate that the use of emotional stimuli affects the depression detection task to some extent and that negative emotional stimuli are more likely to trigger depression-related emotions and to have an impact on the individual’s pronunciation characteristics,thus achieving better detection performance than positive stimuli and neutral speech.