TITLE:
MIES-TR: An Intelligent Model for Real-Time Syllabic Extraction during Keyboard Typing
AUTHORS:
Tchanque Tchakountio Armand, Azanguezet Quimatio Benoît Martin, Geh Wilson Ejuh, Tatsoula Toukem Junie, Chassem Kamdem Priva
KEYWORDS:
Real-Time Syllable Segmentation, Behavioral Biometrics, Continuous Authentication, Keystroke Dynamics, Streaming Neural Networks, Local Attention Mechanism, Adaptive Security
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.12,
December
26,
2025
ABSTRACT: We introduce in this paper MIES-TR, an intelligent model for real-time syllable boundary detection during keyboard typing. This innovative approach positions the syllable as an intermediate biometric unit, combining linguistic richness and motor stability to enhance continuous authentication systems. MIES-TR is built around an optimized neural architecture consisting of character-position encoding, multi-scale convolutions, a unidirectional causal LSTM, and a sliding local attention mechanism. Unlike traditional offline syllabation methods, our model operates in a streaming fashion, without access to future input, and achieves a latency of less than 30 ms per keystroke, enabling dynamic, efficient segmentation compatible with interactive environments. Experimental results on an annotated user corpus demonstrate strong performance, with an average F1-score of 89.9%, word accuracy of 84.2%, and proven inter-user robustness, confirming the relevance of syllabic dynamics as a behavioral identity vector. Beyond accuracy, MIES-TR naturally integrates into adaptive security architectures such as ABAC policies enhanced with dynamic attributes, offering concrete prospects in free typing, multilingual support, multimodal biometric fusion, and embedded device implementation. MIES-TR thus paves the way toward smoother, invisible, and more robust authentication at the intersection of language processing, behavioral biometrics, and real-time cybersecurity.