Gender-Specific Classifiers in Phoneme Recognition and Academic Emotion Detection

摘要

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.