Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
This work addresses depression detection for mental health applications, providing a tool for self-assessment and diagnosis, but it is incremental as it builds on existing multimodal approaches with a new dataset.
The paper tackles automatic depression detection by proposing a method that uses speech and linguistic features from interviews, and introduces the Emotional Audio-Textual Depression Corpus (EATD-Corpus), the first public Chinese dataset with audio and text data. The method achieves state-of-the-art performance on two depression datasets, demonstrating effectiveness and generalization ability.
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.