Modeling hidden dynamics of multimodal cues for spontaneous agreement and disagreement recognition

Abstract

This paper attempts to recognize spontaneous agreement and disagreement based only on nonverbal multi-modal cues. Related work has mainly used verbal and prosodic cues. We demonstrate that it is possible to correctly recognize agreement and disagreement without the use of verbal context (i.e. words, syntax). We propose to explicitly model the complex hidden dynamics of the multimodal cues using a sequential discriminative model, the Hidden Conditional Random Field (HCRF). In this paper, we show that the HCRF model is able to capture what makes each of these social attitudes unique. We present an efficient technique to analyze the concepts learned by the HCRF model and show that these coincide with the findings from social psychology regarding which cues are most prevalent in agreement and disagreement. Our experiments are performed on a spontaneous dataset of real televised debates. The HCRF model outperforms conventional approaches such as Hidden Markov Models and Support Vector Machines.

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