From activities as simple as scheduling a meeting to those as complex as balancing a national budget, people take stances in negotiations and decision making. While the related areas of subjectivity and sentiment analysis have received significant attention, work has focused almost exclusively on text, whereas much stance-taking activity is carried out verbally. Early experiments suggest that people alter their speaking style when engaged in stance-taking, and listeners can much more readily detect negative attitudes by listening to the original speech than by reading transcripts. However, due to the diversity of factors that influence speech production, from individual differences to social context, isolating the signals of stance-taking in speech for automatic recognition presents substantial challenges.
This Early Grant for Exploratory Research project represents a focused exploration of spoken interactions to provide a characterization of linguistic factors associated with stance-taking and develop computational methods that exploit these features to automatically detect stance-taking behavior. Robust linguistic markers of stance-taking are identified through analysis of both controlled elicitations and archived recordings of Congressional hearings on the financial crisis. The former allow experimental comparisons to highlight sometimes subtle contrasts, while the latter enable validation and extension of those findings in real-world, high-stakes discussions. The analysis includes novel acoustic-phonetic measures of dynamic patterns in speech, such as vowel space scaling and pitch/energy velocity, with sophisticated visualization techniques developed to support feature exploration. Findings are validated via stance recognition experiments combining acoustic and lexical cues, which lay the foundation for automatic tracking of trends and shifts in attitudes.
