Driver emotion profiling from speech

Humans sense, perceive, and convey emotion differently from each other due to physical, psychological, environmental, cultural, and language differences. For example, as recognized and studied by psychologists more than a century, it is easier for someone of the same culture to judge and recognize e...

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Main Authors: Kamaruddin, Norhaslinda, Abdul Rahman, Abdul Wahab, Abut, Huseyin
Format: Book Chapter
Language:English
Published: Springer Science+Business Media 2012
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Online Access:http://irep.iium.edu.my/38085/1/Driver_Emotion_Profiling_from_Speech.pdf
http://irep.iium.edu.my/38085/
http://www.springer.com/engineering/signals/book/978-1-4419-9606-0
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Institution: Universiti Islam Antarabangsa Malaysia
Language: English
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spelling my.iium.irep.380852020-07-01T07:50:26Z http://irep.iium.edu.my/38085/ Driver emotion profiling from speech Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Abut, Huseyin BF511 Affection. Feeling. Emotion Humans sense, perceive, and convey emotion differently from each other due to physical, psychological, environmental, cultural, and language differences. For example, as recognized and studied by psychologists more than a century, it is easier for someone of the same culture to judge and recognize emotion correctly compared to those from different culture. In this chapter, we attempt to study the speech emotion recognition problem by using two speech corpora from the Berlin dataset and the NAW datasets. We have investigated the universality as well as diversity of two different cultural speech datasets recorded by German and American speakers, respectively. Experiments were conducted for identifying three basic emotions, namely, angry, sad, and happy with neutral as emotionless state from these datasets. MFCC coefficients were used as feature sets in the experiments, and MLP was employed as classifiers to compare the performance of these datasets. In addition, real-time recorded speech from drivers was also tested to see the performance in a vehicular setting. Finally, speech emotion profiling approach was introduced to explore the universality and diversity of the speech emotion features. Springer Science+Business Media 2012 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/38085/1/Driver_Emotion_Profiling_from_Speech.pdf Kamaruddin, Norhaslinda and Abdul Rahman, Abdul Wahab and Abut, Huseyin (2012) Driver emotion profiling from speech. In: Digital signal processing for in-vehicle systems and safety. Driver Behavior and Modeling Systems . Springer Science+Business Media, Springer New York Dordrecht Heidelberg London, pp. 21-29. ISBN 978-1-4419-9606-0 (P), 978-1-4419-9607-7 (O) http://www.springer.com/engineering/signals/book/978-1-4419-9606-0
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic BF511 Affection. Feeling. Emotion
spellingShingle BF511 Affection. Feeling. Emotion
Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
Abut, Huseyin
Driver emotion profiling from speech
description Humans sense, perceive, and convey emotion differently from each other due to physical, psychological, environmental, cultural, and language differences. For example, as recognized and studied by psychologists more than a century, it is easier for someone of the same culture to judge and recognize emotion correctly compared to those from different culture. In this chapter, we attempt to study the speech emotion recognition problem by using two speech corpora from the Berlin dataset and the NAW datasets. We have investigated the universality as well as diversity of two different cultural speech datasets recorded by German and American speakers, respectively. Experiments were conducted for identifying three basic emotions, namely, angry, sad, and happy with neutral as emotionless state from these datasets. MFCC coefficients were used as feature sets in the experiments, and MLP was employed as classifiers to compare the performance of these datasets. In addition, real-time recorded speech from drivers was also tested to see the performance in a vehicular setting. Finally, speech emotion profiling approach was introduced to explore the universality and diversity of the speech emotion features.
format Book Chapter
author Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
Abut, Huseyin
author_facet Kamaruddin, Norhaslinda
Abdul Rahman, Abdul Wahab
Abut, Huseyin
author_sort Kamaruddin, Norhaslinda
title Driver emotion profiling from speech
title_short Driver emotion profiling from speech
title_full Driver emotion profiling from speech
title_fullStr Driver emotion profiling from speech
title_full_unstemmed Driver emotion profiling from speech
title_sort driver emotion profiling from speech
publisher Springer Science+Business Media
publishDate 2012
url http://irep.iium.edu.my/38085/1/Driver_Emotion_Profiling_from_Speech.pdf
http://irep.iium.edu.my/38085/
http://www.springer.com/engineering/signals/book/978-1-4419-9606-0
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