Journal
PROCEEDINGS OF THE 2018 AUDIO/VISUAL EMOTION CHALLENGE AND WORKSHOP (AVEC'18)
Volume -, Issue -, Pages 23-30Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3266302.3268997
Keywords
Bipolar Disorder Recognition; Multi-scale Temporal Modeling; IncepLSTM; Severity-sensitive Loss
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Funding
- National Natural Science Foundation of China [61673033]
- Research Program of State Key Laboratory of Software Development Environment [SKLSDE-2017ZX-07]
- Microsoft Research Asia Collaborative Program [FY17-RES-THEME033]
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Bipolar disorder (BD) is a prevalent mental illness which has a negative impact on work and social function. However, bipolar symptoms are episodic, especially with irregular variations among different episodes, making BD very difficult to be diagnosed accurately. To solve this problem, this paper presents a novel audio-based approach, called IncepLSTM, which effectively integrates Inception module and Long Short-Term Memory (LSTM) on the feature sequence to capture multi-scale temporal information for BD recognition. Moreover, in order to obtain a discriminative representation of BD severity, we propose a novel severity-sensitive loss based on the triplet loss to model the inter-severity relationship. Considering the small scale of existing BD corpus, to avoid overfitting, we also make use of L1 regulation to improve the sparsity of IncepLSTM. The evaluations are conducted on the Audio/Visual Emotion Challenge (AVEC) 2018 Dataset and the experimental results clearly demonstrate the effectiveness of our method.
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