Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Depression Detection: An Emotional Audio-Textual Corpus and a
  GRU/BiLSTM-based Model   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.

3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th