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作 者:李启飞 王栋[1,2] 高迎明 李雅 LI Qifei;WANG Dong;GAO Yingming;LI Ya(School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Information Science and Engineering,Shandong Normal University,Ji’nan,Shandong 250014,China)
机构地区:[1]北京邮电大学人工智能学院,北京100876 [2]山东师范大学信息科学与工程学院,山东济南250014
出 处:《信号处理》2023年第4期658-666,共9页Journal of Signal Processing
基 金:国家自然科学基金(62271083);模式识别国家重点实验室开放课题基金(202200042)。
摘 要: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.
关 键 词:语音抑郁症检测 情感刺激 特征分析 机器学习 深度学习
分 类 号:TP181[自动化与计算机技术—控制理论与控制工程]
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