Sincerity and deception in speech: Two sides of the same coin? A transfer-and multi-task learning perspective

In this work, we investigate the coherence between inferable deception and perceived sincerity in speech, as featured in the Deception and Sincerity tasks of the INTERSPEECH 2016 Computational Paralinguistics ChallengE (ComParE). We demonstrate an effective approach that combines the corpora of both Challenge tasks to achieve higher classification accuracy. We show that the naïve label mapping method based on the assumption that sincerity and deception are just 'two sides of the same coin', i. e., taking deceptive speech as equivalent to non-sincere speech and vice versa, does not yield satisfactory results. However, we can exploit the interplay and synergies between these characteristics. To achieve this, we combine our previously introduced approach for data aggregation by semi-supervised cross-task label completion with multi-task learning, and knowledge-based instance selection. In the result, our approach achieves significant error rate reductions compared to the official Challenge baseline.