UNDERPIN Mental Disease Dialogue Corpus and Mental Disease Classification Using Audio and Linguistic Features with Feature Importance Analysis

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  • UNDERPIN精神疾患会話コーパスの構築と音声・言語特徴に基づく精神疾患の分類および特徴料分析

Abstract

<p>We have been creating our UNDERPIN mental disease dialogue corpus, which includes more than 1000 hours of recorded voice. Our corpus consists of patients' information (disease name, drugs, etc.), disease test results, and dialogue data. We classified the disease types (bipolar disorder, schizophrenia, dementia, depression, anxiety) versus healthy people, using audio and linguistic features extracted from the corpus. We achieved around 75-91 points in f-score depending on the disease types, which feature importance suggested that formants, fillers, laughs, and questions are important indicators to predict mental diseases.</p>

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