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

Code

• github.com/speechandlanguageprocessing/icassp2022-depression
  OfficialIn paperpytorch★ 203
• github.com/lt-nlp-lab-unibo/ethical-survey-speech
  none★ 5

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.

Tasks

Reproductions