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Deception is a deliberate act to deceive interlocutor by transmitting a message containing false or misleading information. Detection of deception consists in the search for reliable differences between liars and truth-tellers. In this paper, we used the Deceptive Speech Database (DSD) provided for the Deception sub-challenge. DSD consists of deceptive and non-deceptive answers to a set of unknown questions. We have investigated linguistic cues: prosodic cues (pauses and phone duration, speech segmentation) and answer types (e.g., opinion, self-report, offense denial). These cues were automatically detected using the CMU-Sphinx toolkit for speech recognition (acoustic-phonetic decoding, isolated word recognition and keyword spotting). Two kinds of prosodic features were computed from the speech transcriptions (phoneme, silent pause, filled pause, and breathing): the usual speech rate measures and the audio feature based on the multi-resolution paradigm. The answer type features were introduced. A set of answer types was chosen from the transcription of the Training set and each answer type was modeled by a bag-of-words. Experiments have shown improvements of 13.0% and 3.8% on the Development and Test sets respectively, compared to the official baseline Unweighted Average Recall.