Multi-system fusion of extended context prosodic and cepstral features for paralinguistic speaker trait classification

Abstract

As automatic speech processing has matured, research attention has expanded to paralinguistic speech problems that aim to detect beyond-the-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 GMM-UBM, 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.