Sleepiness detection from speech by perceptual features
| Autor: | Bilge Gunsel, Jarek Krajewski, Cenk Sezgin |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Masking (art)
Scheme (programming language) Learning vector quantization Recall Computer science business.industry media_common.quotation_subject Speech recognition Feature extraction Pattern recognition Support vector machine Perception Artificial intelligence Psychoacoustics business computer media_common computer.programming_language |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2013.6637756 |
| Popis: | We propose a two-class classification scheme with a small number of features for sleepiness detection. Unlike the conventional methods that rely on the linguistics content of speech, we work with prosodic features extracted by psychoacoustic masking in spectral and temporal domain. Our features also model the variations between non-sleepy and sleepy modes in a quasi-continuum space with the help of code words learned by a bag-of-features scheme. These improve the unweighted recall rates for unseen people and minimize the language dependence. Recall rates reported based on Karolinska Sleepiness Scale (KSS) for Support Vector Machine and Learning Vector Quantization classifiers show that the developed system enable us monitoring sleepiness efficiently with a lower complexity compared to the reported benchmarking results for Sleepy Language Corpus. |
| Databáze: | OpenAIRE |
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