Effective speech features for cognitive load assessment: classification and regression
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This thesis is about the effectiveness of speech features for cognitive load assessment, with particular attention being paid to new perspectives of this research area. A new cognitive load database, called CoLoSS, is introduced containing speech recordings of users who performed a learning task. Various acoustic features from different categories including prosody, voice quality, and spectrum are investigated in terms of their relevance. Moreover, Teager energy parameters, which have proven highly successful in stress detection, are introduced for cognitive load assessment and it is demonstrated how automatic speech recognition technology can be used to extract potential indicators. The suitability of the extracted features is systematically evaluated by recognition experiments with speaker-independent systems designed for discriminating between three levels of load. Additionally, a novel approach to speech-based cognitive load modelling is introduced, whereby the load is represented as a continuous quantity and its prediction can thus be regarded as a regression problem.
Nákup knihy
Effective speech features for cognitive load assessment: classification and regression, Robert Herms
- Jazyk
- Rok vydání
- 2019
Doručení
Platební metody
2021 2022 2023
Navrhnout úpravu
- Titul
- Effective speech features for cognitive load assessment: classification and regression
- Jazyk
- anglicky
- Autoři
- Robert Herms
- Vydavatel
- Universitätsverlag Chemnitz
- Rok vydání
- 2019
- ISBN10
- 3961000875
- ISBN13
- 9783961000876
- Série
- Wissenschaftliche Schriftenreihe Dissertationen der Medieninformatik
- Kategorie
- Počítače, IT, programování
- Anotace
- This thesis is about the effectiveness of speech features for cognitive load assessment, with particular attention being paid to new perspectives of this research area. A new cognitive load database, called CoLoSS, is introduced containing speech recordings of users who performed a learning task. Various acoustic features from different categories including prosody, voice quality, and spectrum are investigated in terms of their relevance. Moreover, Teager energy parameters, which have proven highly successful in stress detection, are introduced for cognitive load assessment and it is demonstrated how automatic speech recognition technology can be used to extract potential indicators. The suitability of the extracted features is systematically evaluated by recognition experiments with speaker-independent systems designed for discriminating between three levels of load. Additionally, a novel approach to speech-based cognitive load modelling is introduced, whereby the load is represented as a continuous quantity and its prediction can thus be regarded as a regression problem.