Prediction of Deception and Sincerity from Speech Using Automatic Phone Recognition-Based Features

As part of the Interspeech 2016 COMPARE challenge, the two different sub-challenges Deception and Sincerity are addressed. The former refers to the identification of deceptive speech whereas the degree of perceived sincerity of speakers has to be estimated in the latter. In this paper, we investigate the potential of automatic phone recognition-based features for these use case scenarios. The speech transcriptions were used to process the appearing tokens (phoneme, silent pause, filled pause) and the corresponding durations. We designed a high-level feature set including the four groups: vowels, phones, pseudo syllables, and pauses. Additionally, we selected suitable predefined acoustic feature sets and fused them with our introduced features showing a positive effect on the prediction. Moreover, the performance is further boosted by refining these fused features using the ReliefF feature selection method. Experiments show that the final systems outperform the baseline results of both sub-challenges.