Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model

• URL: http://arxiv.org/abs/2202.08210v1
• Date: Tue, 15 Feb 2022 03:29:39 GMT
• Title: Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
• Authors: Ying Shen, Huiyu Yang, Lin Lin
• Abstract summary: Depression is a global mental health problem, the worst case of which can lead to suicide. We propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. We establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers.
• Score: 17.83052349861756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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