Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.

本论文提出了一种基于语音特征和语言内容的新型抑郁症检测方法，建立了一个包含抑郁和非抑郁志愿者的情感音频文本抑郁症语料库，该方法在两个抑郁症数据集上取得了最先进的性能，结果表明了该方法的有效性和泛化能力。

自动抑郁症检测：情感音频文本语料库及GRU/BiLSTM模型