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

TL;DR

This paper introduces a novel depression detection system using speech and text analysis, supported by a new Chinese emotional audio-textual corpus, achieving state-of-the-art results in depression classification.

Contribution

The work presents the first Chinese emotional audio-textual depression dataset and a GRU/BiLSTM-based model that outperforms existing methods in depression detection.

Findings

Achieved state-of-the-art performance on depression datasets.

Demonstrated the effectiveness of speech and linguistic features.

Provided publicly available dataset and source code.

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.