Bipolar Disorder Recognition via Multi-scale Discriminative Audio Temporal Representation

Abstract

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 $L^1$ 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.