Applications of speech recognition have evolved in recent years from simple transcription tasks to metadata analysis. This thesis explores the use of speech recognition for automated fatigue detection. The fatigue detection system relies on accurate phonetic alignments from a speech recognition system. The main challenge addressed in this thesis was to make the process of phonetic alignment using speech recognition robust to out of vocabulary words. This requirement was achieved by incorporating confidence measures, which significantly reduce false positives in speech recognition output. This allowed the performance of the fatigue detection system to match the results of other cognitive tests based on the Sleep Onset Latency (SOL) and Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE). Confidence measures reduced the squared error between voice-based fatigue prediction and SAFTE by 20% when 67.1% of the words in the test set were out of vocabulary words.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-1719 |
| Date | 05 August 2006 |
| Creators | Raghavan, Sridhar |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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