Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice

Vincent P. Martin, Jean Luc Rouas, Florian Boyer, Pierre Philip

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Excessive Daytime Sleepiness (EDS), a symptom linked to chronic sleepiness, impacts everyday life and increases risks of work or road accidents of subjects affected by it. The detection of accident-prone EDS through voice benefits from its ease to be implemented in ecological conditions and to be sober in terms of data processing and costs. Contrary to previous works, this study focuses on long-term sleepiness detection through voice. Using the Multiple Sleep Latency Test corpus, we propose a feature selection pipeline inspired by clinical validation practices to classify accident-prone EDS - as measured by a threshold of 15 on the Epworth Sleepiness Scale - based on vocal clues. We propose three different approaches based on the acoustic quality of voice, reading mistakes, and a whole new approach, relying on Automatic Speech Recognition systems errors. The classification system achieves performances on the same scale as the state-of-the-art systems on short-term sleepiness detection through voice (74.2% of Unweighted Average Recall). Moreover, we give insights into the decision process implied during classification and the system's specificity regarding the threshold delimiting the two classes Higher-risk driver and Lower-risk driver.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages541-545
Number of pages5
ISBN (Electronic)9789082797060
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

Keywords

  • Accidental risk
  • Automatic speech recognition
  • Excessive daytime sleepiness
  • Sleepiness
  • Voice

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