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

研究成果: Conference article

3 引文 (Scopus)

摘要

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.

原文English
頁(從 - 到)1452-1456
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
08-12-September-2016
DOIs
出版狀態Published - 2016 一月 1
事件17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
持續時間: 2016 九月 82016 九月 16

指紋

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

引用此文

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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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AU - Jang, Fong Lin

PY - 2016/1/1

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N2 - 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.

AB - 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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