Abstract
We report on machine learning experiments to distinguish deceptive from nondeceptive speech in the Columbia-SRI-Colorado (CSC) corpus. Specifically, we propose a system combination approach using different models and features for deception detection. Scores from an SVM system based on prosodic/lexical features are combined with scores from a Gaussian mixture model system based on acoustic features, resulting in improved accuracy over the individual systems. Finally, we compare results from the prosodic-only SVM system using features derived either from recognized words or from human transcriptions.
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