Comparing approaches for mitigating intergroup variability in personality recognition
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Personality have been found to predict many life outcomes, and there have been huge interests on automatic personality recognition from a speaker's utterance. Previously, we achieved accuracies between 37 classification of high, medium or low for each of the Big Five personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreeableness, Neuroticism). We show here that we can improve performance on this task by accounting for heterogeneity of gender and L1 in our data, which has English speech from female and male native speakers of Chinese and Standard American English (SAE). We experiment with personalizing models by L1 and gender and normalizing features by speaker, L1 group, and/or gender.
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