AES Europe 2026

This study presents a voice-centered machine learning
framework for detecting mental fatigue in military
personnel, integrating acoustic analysis with physiological
biosensors to enhance detection robustness. Mental fatigue
poses critical safety; performance challenges in
military operations, yet cultural stigma often prevents
self-reporting. We collected multi-modal data from 23
participants across two fatigue states, extracting
comprehensive acoustic features including sound pressure
level (SPL), formants, mel-frequency cepstral coefficients
(MFCCs), jitter, shimmer, harmonic-to-noise ratio (HNR),
; temporal speech characteristics. These voice features
were combined with electroencephalography (EEG),
photoplethysmography (PPG),; temperature data to train
multiple machine learning classifiers. The voice-based
models achieved accuracies between 82-85\%, with support
vector machines (SVM); long short-term memory (LSTM)
networks demonstrating superior performance. When acoustic
features were combined with physiological markers,
classification accuracy improved to 92\%, with
Classification; Regression Trees (CART); Linear
Discriminant Analysis (LDA) emerging as top performers.
Statistical analysis identified SPL; formant variance as
the most discriminative voice features, while Lempel-Ziv
Complexity (LZC); theta/beta ratio proved most reliable
for EEG. Evaluation on new participants yielded 67\%
accuracy, revealing model generalization challenges that
inform future research directions. This work demonstrates
that voice-based machine learning systems, when augmented
with physiological data, offer a promising non-invasive
approach to real-time fatigue monitoring in operational
military environments.