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Previous work in detection of deceptive speech has largely focused on prosodic, vocal tract, and lexical features. Glottal waveform features have been shown to be useful discriminators for various types of speaker affect and warrant further study within the context of deception detection. This paper reports on speaker-dependent machine learning and feature selection experiments for classifying deceptive and non- deceptive speech using a large number of statistical features derived from the glottal waveform. We present current results comparing the classification performance and selected feature sets across 19 speakers from the Columbia-SRI-Colorado corpus of deceptive speech and discuss directions for future work.