Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 4
July-August 2026

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CHGAF-Net: A Foundational Framework for Hidden Distress Detection Through Confidence-Aware Multimodal Fusion

Author(s) Ms. Anusha Thalla
Country India
Abstract In recent years, women’s safety remains a critical societal concern where individuals experiencing distress often unable to explicitly communicate their emotional state during emergency situations. Existing models advanced in speech emotion recognition and Natural Language Processing (NLP) enabled automates distress analysis. However, these existing approaches predominantly rely on unimodal learning or static multimodal fusion strategies which limited their ability to effectively capture complementary emotional and semantic cues while adapting to varying modality reliability. To address these challenges, a Confidence-Aware Hierarchical Gated Attention Fusion Network (CHGAF-Net) is proposed for hidden distress detection. The speech-derived features such as pitch, energy, spectral characteristics and Mel-Frequency Cepstral Coefficients (MFCCs) are extracted. Consequently, the context embeddings are extracted with Distilled Bi-directional Encoder Representations from Transformer (DistilBERT). Then, these features are fused with a confidence-aware gated attention fusion mechanism. Further, a custom multimodal distress dataset comprising of 48 samples were categorized as safe, warning or danger classes using a fully connected classifier. From the results, the proposed CHGAF-Net attained results in terms of accuracy 70%, precision 74%, recall 70% and F1-score 70% respectively. Qualitative and quantitative analyses further confirm the capability of the proposed framework to capture meaningful distress patterns across multiple modalities.
Keywords Confidence-Aware Multimodal Fusion, Custom Dataset, Distress Detection, Foundational Framework, Women Safety.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-06-25
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E-ISSN 3048-7641
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