INTERSPEECH 2014 Manipulating stance and involvement using collaborative tasks: An exploratory comparison Valerie Freeman1, Julian Chan1, Gina-Anne Levow1, Richard Wright1, Mari Ostendorf2,Victoria Zayats2 1Department of Linguistics 2Department of Electrical Engineering University of Washington Seattle, WA USA fvalerief,jchan3,levow,rawright,ostendor,
[email protected] Abstract us to identify and quantify properties of the speech signal as- sociated with stance-taking, create an acoustic model of stance, The ATAROS project aims to identify acoustic signals of and test theories of stance-taking on natural speech. stance-taking in order to inform the development of automatic stance recognition in natural speech. Due to the typically low In automatic recognition research, stance links most closely frequency of stance-taking in existing corpora that have been to sentiment and subjectivity, expressions of a “private state” used to investigate related phenomena such as subjectivity, we [5], an internal mental or emotional state. Research on sen- are creating an audio corpus of unscripted conversations be- timent and subjectivity analysis has exploded since the publi- tween dyads as they complete collaborative tasks designed to cation of foundational work such as [6, 7]. The majority of elicit a high density of stance-taking at increasing levels of in- this work has focused on textual materials with accompany- volvement. To validate our experimental design and provide a ing annotated corpora, such as those described in [7, 8, 6] and preliminary assessment of the corpus, we examine a fully tran- many others. Such text-based approaches to subjectivity recog- scribed and time-aligned portion to compare the speaking styles nition primarily exploit lexical and syntactic evidence, relying in two tasks, one expected to elicit low involvement and weak on long, well-formed sentences and clauses for identification of stances, the other high involvement and strong stances.