To date, studies of deceptive speech have largely been confined to descriptive studies and observations from subjects, researchers, or practitioners, with few empirical studies of the specific lexical or acoustic/prosodic features which may characterize deceptive speech. We present results from a study seeking to distinguish deceptive from non-deceptive speech using machine learning techniques on features extracted from a large corpus of deceptive and non-deceptive speech. This corpus employs an interview paradigm that includes subject reports of truth vs. lie at multiple temporal scales. We present current results comparing the performance of acoustic/prosodic, lexical, and speaker-dependent features and discuss future research directions.