12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Identifying Agreement/Disagreement in Conversational Speech: A Cross-Lingual Study

Wen Wang (1), Kristin Precoda (1), Colleen Richey (1), Geoffrey Raymond (2)

(1) SRI International, USA
(2) University of California at Santa Barbara, USA

This paper presents models for detecting agreement/ disagreement between speakers in English and Arabic broadcast conversation shows. We explore a variety of features, including lexical, structural, durational, and prosodic features. We experiment with these features using Conditional Random Fields models and conduct systematic investigations on efficacy of various feature groups across languages. Sampling approaches are examined for handling highly imbalanced data. Overall, we achieved 79.2% (precision), 50.5% (recall), 61.7% (F1) for agreement detection and 69.2% (precision), 46.9% (recall), and 55.9% (F1) for disagreement detection, on English broadcast conversation data; and 89.2% (precision), 30.1% (recall), 45.1% (F1) for agreement detection and 75.9% (precision), 28.4% (recall), and 41.3% (F1) for disagreement detection, on Arabic broadcast conversation data.

Full Paper

Bibliographic reference.  Wang, Wen / Precoda, Kristin / Richey, Colleen / Raymond, Geoffrey (2011): "Identifying agreement/disagreement in conversational speech: a cross-lingual study", In INTERSPEECH-2011, 3093-3096.