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- W4224924082 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." @default.
- W4224924082 created "2022-04-28" @default.
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- W4224924082 date "2022-05-23" @default.
- W4224924082 modified "2023-09-30" @default.
- W4224924082 title "Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru/Bilstm-Based Model" @default.
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- W4224924082 doi "https://doi.org/10.1109/icassp43922.2022.9746569" @default.
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