Gender-specific classifiers in phoneme recognition and academic emotion detection

Gender-specific classifiers are shown to outperform general classifiers. In calibrated experiments designed to demonstrate this, two sets of data were used to build male-specific and female-specific classifiers. The first dataset is used to predict vowel phonemes based on speech signals, and the sec...

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Main Authors: Azcarraga, Arnulfo P., Talavera, Arces, Azcarraga, Judith
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Published: Animo Repository 2016
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/1280
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-22792022-11-16T02:41:16Z Gender-specific classifiers in phoneme recognition and academic emotion detection Azcarraga, Arnulfo P. Talavera, Arces Azcarraga, Judith Gender-specific classifiers are shown to outperform general classifiers. In calibrated experiments designed to demonstrate this, two sets of data were used to build male-specific and female-specific classifiers. The first dataset is used to predict vowel phonemes based on speech signals, and the second dataset is used to predict negative emotions based on brainwave (EEG) signals. A Multi-Layered-Perceptron (MLP) is first trained as a general classifier, where all data from both male and female users are combined. This general classifier recognizes vowel phonemes with a baseline accuracy of 91.09%, while that for EEG signals has an average baseline accuracy of 58.70%. The experiments show that the performance significantly improves when the classifiers are trained to be gender-specific–that is, there is a separate classifier for male users, and a separate classifier for female users. For the vowel phoneme recognition dataset, the average accuracy increases to 94.20% and 95.60%, for male only users and female-only users, respectively. As for the EEG dataset, the accuracy increases to 65.33% for male-only users and to 70.50% for female-only users. Performance rates using recall and precision show the same trend. A further probe is done using SOM to visualize the distribution of the sub-clusters among male and female users. © Springer International Publishing AG 2016. 2016-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1280 Faculty Research Work Animo Repository Phonemic awareness Emotion recognition Sex differences Electroencephalography Classifiers (Linguistics) Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Phonemic awareness
Emotion recognition
Sex differences
Electroencephalography
Classifiers (Linguistics)
Computer Sciences
spellingShingle Phonemic awareness
Emotion recognition
Sex differences
Electroencephalography
Classifiers (Linguistics)
Computer Sciences
Azcarraga, Arnulfo P.
Talavera, Arces
Azcarraga, Judith
Gender-specific classifiers in phoneme recognition and academic emotion detection
description Gender-specific classifiers are shown to outperform general classifiers. In calibrated experiments designed to demonstrate this, two sets of data were used to build male-specific and female-specific classifiers. The first dataset is used to predict vowel phonemes based on speech signals, and the second dataset is used to predict negative emotions based on brainwave (EEG) signals. A Multi-Layered-Perceptron (MLP) is first trained as a general classifier, where all data from both male and female users are combined. This general classifier recognizes vowel phonemes with a baseline accuracy of 91.09%, while that for EEG signals has an average baseline accuracy of 58.70%. The experiments show that the performance significantly improves when the classifiers are trained to be gender-specific–that is, there is a separate classifier for male users, and a separate classifier for female users. For the vowel phoneme recognition dataset, the average accuracy increases to 94.20% and 95.60%, for male only users and female-only users, respectively. As for the EEG dataset, the accuracy increases to 65.33% for male-only users and to 70.50% for female-only users. Performance rates using recall and precision show the same trend. A further probe is done using SOM to visualize the distribution of the sub-clusters among male and female users. © Springer International Publishing AG 2016.
format text
author Azcarraga, Arnulfo P.
Talavera, Arces
Azcarraga, Judith
author_facet Azcarraga, Arnulfo P.
Talavera, Arces
Azcarraga, Judith
author_sort Azcarraga, Arnulfo P.
title Gender-specific classifiers in phoneme recognition and academic emotion detection
title_short Gender-specific classifiers in phoneme recognition and academic emotion detection
title_full Gender-specific classifiers in phoneme recognition and academic emotion detection
title_fullStr Gender-specific classifiers in phoneme recognition and academic emotion detection
title_full_unstemmed Gender-specific classifiers in phoneme recognition and academic emotion detection
title_sort gender-specific classifiers in phoneme recognition and academic emotion detection
publisher Animo Repository
publishDate 2016
url https://animorepository.dlsu.edu.ph/faculty_research/1280
_version_ 1751550430941282304