A speech emotion recognition model fusing Wav2Vec2, pitch, and MFCC features with dual attention achieved 79.13% F1 on hotline negative emotion classification and 73.30% on a Vietnamese benchmark, but suicide-group emotional differences were non-significant.
Follow-up with callers to the national suicide prevention lifeline: Evaluation of callers’ perceptions of care,
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Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines
A speech emotion recognition model fusing Wav2Vec2, pitch, and MFCC features with dual attention achieved 79.13% F1 on hotline negative emotion classification and 73.30% on a Vietnamese benchmark, but suicide-group emotional differences were non-significant.