Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines
Journal:
arXiv
Published Date:
Jan 15, 2025
Abstract
Mental health is a critical global public health issue, and psychological
support hotlines play a pivotal role in providing mental health assistance and
identifying suicide risks at an early stage. However, the emotional expressions
conveyed during these calls remain underexplored in current research. This
study introduces a method that combines pitch acoustic features with deep
learning-based features to analyze and understand emotions expressed during
hotline interactions. Using data from China's largest psychological support
hotline, our method achieved an F1-score of 79.13% for negative binary emotion
classification.Additionally, the proposed approach was validated on an open
dataset for multi-class emotion classification,where it demonstrated better
performance compared to the state-of-the-art methods. To explore its clinical
relevance, we applied the model to analysis the frequency of negative emotions
and the rate of emotional change in the conversation, comparing 46 subjects
with suicidal behavior to those without. While the suicidal group exhibited
more frequent emotional changes than the non-suicidal group, the difference was
not statistically significant.Importantly, our findings suggest that emotional
fluctuation intensity and frequency could serve as novel features for
psychological assessment scales and suicide risk prediction.The proposed method
provides valuable insights into emotional dynamics and has the potential to
advance early intervention and improve suicide prevention strategies through
integration with clinical tools and assessments The source code is publicly
available at https://github.com/Sco-field/Speechemotionrecognition/tree/main.
