Gender-Specific Classifiers in Phoneme Recognition and Academic Emotion Detection

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.