'Prediction of Sleepiness Ratings from Voice by Man and Machine': A Perceptual Experiment Replication Study

Vincent P. Martin, Aymeric Ferron, Jean Luc Rouas, Pierre Philip

Research output: Contribution to journalConference articlepeer-review

Abstract

Following the release of the SLEEP corpus during the Interspeech 2019 paralinguistic continuous sleepiness estimation challenge, a paper presented at Interspeech 2020 by Huckvale et al. examined the reasons for the poor performance of the models proposed for this task. Careful analyses of the corpus led to the conclusion that its bias makes it hazardous to use for training machine learning systems, but a perceptual experiment on a subset of this corpus seemed to indicate that human hearing is however able to estimate sleepiness on this corpus.In this study, we present the results of the Endymion replication study, in which the same samples were rated by thirty French-speaking naive listeners. We then discuss the causes of the differences between the two studies and examine the effect of listener and sample characteristics on annotation performances.

Original languageEnglish
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Keywords

  • Experimental study replication
  • Paralinguistic
  • Perceptual study
  • Sleepiness
  • Voice

Fingerprint

Dive into the research topics of ''Prediction of Sleepiness Ratings from Voice by Man and Machine': A Perceptual Experiment Replication Study'. Together they form a unique fingerprint.

Cite this