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

Kun Yi Huang, Chung-Hsien Wu, Yu Ting Kuo, Fong Lin Jang

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1452-1456
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume08-12-September-2016
DOIs
Publication statusPublished - 2016 Jan 1
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: 2016 Sep 82016 Sep 16

Fingerprint

Mood
Elicitation
Model structures
Disorder
Latent Semantic Analysis
Classifiers
Semantics
Likelihood
Classifier
Model
Memory Term
K-means Algorithm
Mood Disorders
Bipolar Disorder
Affective
Emotion
Model-based
Speech
Experimental Results

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this

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title = "Unipolar depression vs. bipolar disorder: An elicitation-based approach to short-term detection of mood disorder",
abstract = "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.",
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Unipolar depression vs. bipolar disorder : An elicitation-based approach to short-term detection of mood disorder. / Huang, Kun Yi; Wu, Chung-Hsien; Kuo, Yu Ting; Jang, Fong Lin.

In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Vol. 08-12-September-2016, 01.01.2016, p. 1452-1456.

Research output: Contribution to journalConference article

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