Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection

Automatic deception detection is an important problem with far-reaching implications for many disciplines. We present a series of experiments aimed at automatically detecting deception from speech. We use the Columbia X-Cultural Deception (CXD) Corpus, a large-scale corpus of within-subject deceptive and non-deceptive speech, for training and evaluating our models. We compare the use of spectral, acoustic-prosodic, and lexical feature sets, using different machine learning models. Finally, we design a single hybrid deep model with both acoustic and lexical features trained jointly that achieves state-of-the-art results on the CXD corpus.