Unipolar depression vs. bipolar disorder: An elicitation-based approach to short-term detection of mood disorder

Mood disorders include unipolar depression (UD) and bipolar disorder (BD). In this work, an elicitation-based approach to short-term detection of mood disorder based on the elicited speech responses is proposed. First, a long-short term memory (LSTM)-based classifier was constructed to generate the emotion likelihood for each segment in the elicited speech responses. The emotion likelihoods were then clustered into emotion codewords using the K-means algorithm. Latent semantic analysis (LSA) was then adopted to model the latent relationship between the emotion codewords and the elicited responses. The structural relationships among the emotion codewords in the LSA-based matrix were employed to construct a latent affective structure model (LASM) for characterizing each mood. For mood disorder detection, the similarity between the input speech LASM and each of the mood-specific LASMs was estimated. Finally, the mood with its LASM most similar to the input speech LASM is regarded as the detected mood. Experimental results show that the proposed LASM-based method achieved 73.3%, improving the detection accuracy by 13.3% compared to the commonly used SVM-base classifiers.