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Cited by 48Sleep and Work-Related FatigueSpeech Recognition and SynthesisEmotion and Mood Recognition

Detecting Fatigue From Voice Using Speech Recognition

Greeley, H.P., Friets, E., Wilson, J.P., Raghavan, S., Picone, J., Berg, J.·2006·2006 IEEE International Symposium on Signal Processing and Information Technology
DOI 10.1109/isspit.2006.270865
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Abstract

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

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  • Greeley et al. (2006) found a circadian pattern in MFCCs due to sleep deprivation.

    “Also Greeley et al. (2006) found a circadian pattern in MFCCs due to sleep deprivation.”

    From Voice Stress Analysis: A New Framework for Voice and Effort in Human Performance (2018) · cites this paper as Greeley et al. (2006)

  • Greeley et al. (2006) found a strong correlation (r = -0.89) for the phoneme 'p' and for 't' (r = -0.67) between sleep onset latency determined by EEG and MFCC voice parameters.

    “A strong correlation (r = -0.89) for the phoneme ‘p’ and for ‘t’ (r = -0.67) was found (Greeley et al., 2006).”

    From Voice Stress Analysis: A New Framework for Voice and Effort in Human Performance (2018) · cites this paper as Greeley et al. (2006)

  • In both sleep deprivation studies, it has been concluded that voice acoustics are promising in the detection of sleep-deprivation.

    “In both studies, it has been concluded that voice acoustics are promising in the detection of sleep-deprivation.”

    From Voice Stress Analysis: A New Framework for Voice and Effort in Human Performance (2018) · cites this paper as Greeley et al. (2006)

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Authors
Greeley, H. P., Friets, E., Wilson, J. P., Raghavan, S., Picone, J., & Berg, J.
Affiliations
  • Creare (United States) (US)
  • Mississippi State University (US)
  • MathWorks (United States) (US)
ORCID
Venue
2006 IEEE International Symposium on Signal Processing and Information Technology
Publisher
IEEE
Published
2006-08
Pages
567-571
DOI
10.1109/isspit.2006.270865
OpenAlex
W2158555439
Cited by
48
References
1
Citations by year
2025: 3 · 2024: 4 · 2023: 1 · 2022: 5 · 2021: 8 · 2020: 4 · 2019: 4 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 3 · 2014: 1 · 2013: 2
FWCI
0.70
Type
conference paper
Language
en
Open access
closed
Locations
IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (publishedVersion)
Funders
Air Force Research Laboratory
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
fatigue detection, speech recognition, phonetic alignment, out-of-vocabulary words, confidence measures, sleep latency, military personnel, non-intrusive monitoring

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