Sleepiness detection from speech by perceptual features

Günsel, Bilge; Sezgin, Cenk; Krajewski, Jarek

doi:10.1109/icassp.2013.6637756

Cited by 6 publications

(15 citation statements)

References 9 publications

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“…The KSS being a semi-continuous measure, the choice is made to split the dataset into two classes: following [4], [6], [9], [15], the samples with a mean KSS>7.5 will be considered as Sleepy Language (SL). On the contrary, the samples with a KSS≤7.5 are labelled as Non Sleepy Language (NSL).…”

Section: Ground Truth: the Karolinska Sleeping Scalementioning

confidence: 99%

Sleepiness detection on read speech using simple features

Martin,

Rouas,

Thivel

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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Section: Ground Truth: the Karolinska Sleeping Scalementioning

confidence: 99%

Sleepiness detection on read speech using simple features

Martin,

Rouas,

Thivel

et al. 2019

2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

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“…Another version of this type of model has been developed with to predict “microsleeps” with similar success (Krajewski et al, 2008 ). Other variations also exist, such as the model developed by Günsel et al ( 2013 ), which uses prosodic features extracted by psychoacoustic masking, and the model developed by Thakare ( 2014 ), which utilizes automatic speech recognition (ASR) to identify key phonemes from which to extract features. This latter approach would require a robust ASR system in operational environments to account for environmental noise, reverberation, and channel distortions.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fatigue and Psychophysiological Test Performance from Speech for Safety-Critical Environments

Baykaner

Huckvale

Whiteley

et al. 2015

Front. Bioeng. Biotechnol.

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Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the prediction of fatigue from speech have been limited because of their reliance on subjective ratings and because they lack comparison to other methods for assessing fatigue. In this paper, we present an analysis of voice recordings and psychophysiological test scores collected from seven aerospace personnel during a training task in which they remained awake for 60 h. We show that voice features and test scores are affected by both the total time spent awake and the time position within each subject’s circadian cycle. However, we show that time spent awake and time-of-day information are poor predictors of the test results, while voice features can give good predictions of the psychophysiological test scores and sleep latency. Mean absolute errors of prediction are possible within about 17.5% for sleep latency and 5–12% for test scores. We discuss the implications for the use of voice as a means to monitor the effects of fatigue on cognitive performance in practical applications.

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“…In 2011 the challenge was to classify speech recordings into sleepy vs non-sleepy, using KSS ratings of 7 and below as non-sleepy, and 8 and above as sleepy. In that challenge the baseline systems achieved an accuracy of about 70%, while a later study using the same speech data and task achieved a classification accuracy of over 80% [4]. Could the failure of the 2019 challenge be because of the switch from a classification task to a regression task?…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sleepiness Ratings from Voice by Man and Machine

Huckvale

Beke

Ikushima³

2020

Interspeech 2020

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This paper looks in more detail at the Interspeech 2019 computational paralinguistics challenge on the prediction of sleepiness ratings from speech. In this challenge, teams were asked to train a regression model to predict sleepiness from samples of the Düsseldorf Sleepy Language Corpus (DSLC). This challenge was notable because the performance of all entrants was uniformly poor, with even the winning system only achieving a correlation of r=0.37. We look at whether the task itself is achievable, and whether the corpus is suited to training a machine learning system for the task. We perform a listening experiment using samples from the corpus and show that a group of human listeners can achieve a correlation of r=0.7 on this task, although this is mainly by classifying the recordings into one of three sleepiness groups. We show that the corpus, because of its construction, confounds variation with sleepiness and variation with speaker identity, and this was the reason that machine learning systems failed to perform well. We conclude that sleepiness rating prediction from voice is not an impossible task, but that good performance requires more information about sleepy speech and its variability across listeners than is available in the DSLC corpus.

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Sleepiness detection from speech by perceptual features

Cited by 6 publications

References 9 publications

Sleepiness detection on read speech using simple features

Sleepiness detection on read speech using simple features

Predicting Fatigue and Psychophysiological Test Performance from Speech for Safety-Critical Environments

Prediction of Sleepiness Ratings from Voice by Man and Machine

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