Can I read Attention Guided Learnable Time-domain Filterbanks for Speech Depression Detection on EtoBox?

Attention Guided Learnable Time-domain Filterbanks for Speech Depression Detection by Wenju Yang; Jiankang Liu; Peng Cao; Rongxin Zhu; Yang Wang; Jian K. Liu; Fei Wang; Xizhe Zhang is a Computer Science article available to read on EtoBox.

What is Attention Guided Learnable Time-domain Filterbanks for Speech Depression Detection about?

Depression, as a global mental health problem, is lacking effective screening methods that can help with early detection and treatment. This paper aims to facilitate the large-scale screening of depression by focusing on the speech depression detection (SDD) task. Currently, direct modeling on the raw signal yields a large number of parameters, and the existing deep learning-based SDD models mainly use the fixed Mel-scale spectral features as input. However, these features are not designed for depression detection, and the manual settings limit the exploration of fine-grained feature representations. In this paper, we learn the effective representations of the raw signals from an interpretable perspective. Specifically, we present a joint learning framework with attention-guided learnable time-domain filterbanks for depression classification (DALF), which collaborates with the depression filterbanks features learning (DFBL) module and multi-scale spectral attention learning (MSSA) module. DFBL is capable of producing biologically meaningful acoustic features by employing learnable time-domain filters, and MSSA is used to guide the learnable filters to better retain the useful frequ

Who reads Attention Guided Learnable Time-domain Filterbanks for Speech Depression Detection?

Author

Wenju Yang; Jiankang Liu; Peng Cao; Rongxin Zhu; Yang Wang; Jian K. Liu; Fei Wang; Xizhe Zhang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)