Detecting Fatigue From Voice Using Speech Recognition

Military and civilian experience has shown that long-duration assignments present increased risk of performance failures as the mission progresses. This is due to interruption of normal sleep cycles and psychological pressures of the work environment. There continues to be a need for a non-intrusive fatigue assessment system to successfully monitor the level of alertness of personnel during critical missions and activities. Experimental results on human voice show that specific phones have a predictable dependence on fatigue. Hence, precise phonetic identification and alignment are important to voice-based fatigue detection. This paper explores techniques for detecting fatigue from voice using speech recognition to obtain phonetic alignments. A confidence measure was used to filter out less likely word hypotheses from the ASR's output. In this paper we restricted our analysis to dealing with out-of-vocabulary words. The results obtained from voice show strong correlation with other standardized tests such as sleep onset latency and sleep, activity, fatigue, and task effectiveness