Toward Interpretable and Real-time Speech Biomarkers of Stress Using Adaptive Sub-band Attention

Speech carries physiological indicators of stress through changes in respiration, vocal-fold tension, and articulatory stability.Conventional representations such as MFCCs and i-vectors smooth octave-level spectral variations and remain sensitive to speaker and noise variability.This study introduces an Adaptive Spectral-Vector (ASV) framework combining spectral contrast, Karhunen-Loève transformation, and i-vector modeling with a novel Adaptive Sub-Band Attention (ASBA) mechanism that adaptively enhances stress-relevant frequency regions while suppressing noise.Evaluations on SUSAS, Emo-DB, and RAVDESS using speaker-independent, cross-corpus, noiseadded, and low-resource settings show strong performance: 91.7% intra-corpus accuracy, ≥82% cross-corpus accuracy, and 82.1% at 5 dB SNR.ASBA attention weights correlate significantly (p < 0.01) with physiological acoustic markers, supporting interpretability.With ~0.61M parameters and