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In this paper we investigate the problem of user authentication using keystroke biometrics. A new distance metric that is effective in dealing with the challenges intrinsic to keystroke dynamics data, i.e., scale variations, feature interactions and redundancies, and outliers is proposed. Our keystroke biometrics algorithms based on this new distance metric(More)
Accelerometers embedded in mobile devices have shown great potential for non-obtrusive gait biometrics by directly capturing a user's characteristic locomotion. Although gait analysis using these sensors has achieved highly accurate authentication and identification performance under controlled experimental settings, the robustness of such algorithms in the(More)
The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech(More)
Automated operations based on voice commands would become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under nonstationary and chaotic noise environments. In this research, we tried to significantly improve the speech recognition rates under(More)
Parallel isolated word corpora were collected from healthy speakers and individuals with speech impairment due to stroke or cerebral palsy. Surface electromyographic (sEMG) signals were collected for both vocalized and mouthed speech production modes. Pioneering work on disordered speech recognition using the acoustic signal, the sEMG signals, and their(More)
The performance of speech recognition systems depends on consistent quality of the speech features across variable environmental conditions encountered during training and evaluation. This paper presents a kernel-based nonlinear predictive coding procedure that yields speech features which are robust to nonstationary noise contaminating the speech signal.(More)
sEMG based silent speech recognition systems seek to bypass the limitations of acoustic speech recognition by measuring and interpreting muscle activity of the facial and neck musculature involved in speech production. However, this speech recognition modality introduces unique challenges of its own. This paper describes signal acquisition and processing(More)