Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan.
PST Inc., Yokohama 231-0023, Japan.
Int J Environ Res Public Health. 2023 Feb 23;20(5):3965. doi: 10.3390/ijerph20053965.
Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients' distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.
基于语音的抑郁检测方法已在全球范围内被研究，作为一种客观且易于检测抑郁的方法。传统研究评估抑郁的存在或严重程度。然而，对症状的评估不仅是治疗抑郁症所必需的技术，也是缓解患者痛苦所必需的技术。因此，我们研究了一种从抑郁患者的 HAM-D 评分中聚类症状的方法，并通过基于他们语音的声学特征来估计不同症状组的患者。我们可以以 79%的准确率将不同的症状组分开。结果表明，语音可以估计与抑郁相关的症状。
