Gender-specific classifiers in phoneme recognition and academic emotion detection

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

9950 LNCS

First Page

497

Last Page

504

Publication Date

1-1-2016

Abstract

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.

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Digitial Object Identifier (DOI)

10.1007/978-3-319-46681-1_59

Disciplines

Computer Sciences

Keywords

Phonemic awareness; Emotion recognition; Sex differences; Electroencephalography; Classifiers (Linguistics)

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