As automatic speech processing has matured, research attention has expanded to paralinguistic speech problems that aim to detect "beyondthe- words" information. This paper focuses on the identification of seven speaker trait categories from the Interspeech Speaker Trait Challenge: likeability, intelligibility, openness, conscientiousness, extraversion, agreeableness, and neuroticism. Our approach combines multiple features including prosodic, cepstral, shifted-delta cepstral, and a reduced set of the OpenSMILE features. Our classification approaches included GMMUBM, eigenchannel, support vector machines, and distance based classifiers. Optimized feature reduction and logistic regression-based score calibration and fusion led to results that perform competitively against the challenge baseline in all categories.

Index Terms: speaker traits, prosody, MFCCs, Gaussian mixture modeling