The acoustic pressure microphone has served as the primary instrument for collecting speech data for automatic speech recognition systems. The acoustic microphone suffers from limitations, such as sensitivity to background noise and relatively far proximity to speech production organs. Alternative speech collection sensors may serve to enhance the effectiveness of automatic speech recognition systems. In this study, we first consider an experimental evaluation of the TEO-CB-AutoEnv feature in an actual law enforcement training scenario. We consider feature relation to stress level assessment over time. Next, we explore the use of the physiological microphone, a gel-based device placed next to the vocal folds on the outside of the throat used to measure vibrations of the vocal tract and minimize background noise, as we investigate the effectiveness of a TEO-CB-AutoEnv-based automatic stress recognition system. We employ both acoustic and physiological sensors as stand-alone speech data collection devices as well as consider both sensors concurrently. For the latter, we devise a weighted composite decision scheme using both the acoustic and physiological microphone data that yields relative average error rate reductions of 32% and 6% versus sole employment of acoustic and physiological microphone data, respectively, in a realistic stressful environment.
Cite as: Ruzanski, E., Hansen, J.H.L., Finan, D., Meyerhoff, J., Norris, W., Wollert, T. (2005) Improved "TEO" feature-based automatic stress detection using physiological and acoustic speech sensors. Proc. Interspeech 2005, 2653-2656, doi: 10.21437/Interspeech.2005-252
@inproceedings{ruzanski05_interspeech,
author={Evan Ruzanski and John H. L. Hansen and Don Finan and James Meyerhoff and William Norris and Terry Wollert},
title={{Improved "TEO" feature-based automatic stress detection using physiological and acoustic speech sensors}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2653--2656},
doi={10.21437/Interspeech.2005-252}
}