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ATAROS Technical Report 1: Corpus Collection and Initial Task Validation

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ATAROS Technical Report 1: Corpus Collection and Initial Task Validation ATAROS Technical Report 1: Corpus collection and initial task validation Valerie Freeman1, Julian Chan1, Gina-Anne Levow1, Richard Wright1, Mari Ostendorf2, Victoria Zayats2, Yi Luan2, Heather Morrison1, Lauren Fox1 Maria Antoniak1, Phoebe Parsons1 1Department of Linguistics 2Department of Electrical Engineering University of Washington Seattle, WA USA valerief,jchan3,levow,rawright,ostendor,vzayats,luanyi, hmorri83,laurenf7,riamarie,[email protected] This technical report describes the ATAROS (Automatic constrained to fine-grained content analysis, which often re- Tagging and Recognition of Stance) corpus design, collection, lies on subjective interpretation. An exception is the precur- and transcription procedures, as well as current work in sor to the current project [3, 4], which drew on existing frame- progress: stance annotation protocols and task validation works in content analysis to identify areas for phonetic com- measures. Portions of this work were presented at the ASA parison. As some of the first work to focus on acoustic prop- 2014 Spring Meeting and are under review for Interspeech erties of stance-taking, it found that stance-expressing phrases 2014. had faster speaking rates, longer stressed vowels, and more ex- panded vowel spaces when compared to stance-neutral phrases. Such acoustically-measurable properties are the target of inves- Abstract tigation in the ATAROS (Automatic Tagging and Recognition The ATAROS project aims to identify acoustic signals of of Stance) project. stance-taking in order to inform the development of automatic stance recognition in natural speech. Due to the typically low frequency of stance-taking in existing corpora that have been In automatic recognition research, stance links most closely used to investigate related phenomena such as subjectivity, we to sentiment and subjectivity, expressions of a “private state” are creating an audio corpus of unscripted conversations be- [5], an internal mental or emotional state. Research on sen- tween dyads as they complete collaborative tasks designed to timent and subjectivity analysis has exploded since the publi- elicit a high density of stance-taking at increasing levels of in- cation of foundational work such as [6, 7]. The majority of volvement. To validate our experimental design and provide a this work has focused on textual materials with accompany- preliminary assessment of the corpus, we examine a fully tran- ing annotated corpora, such as those described in [7, 8, 6] and scribed and time-aligned portion to compare the speaking styles many others. Such text-based approaches to subjectivity recog- in two tasks, one expected to elicit low involvement and weak nition primarily exploit lexical and syntactic evidence, relying stances, the other high involvement and strong stances. We find on long, well-formed sentences and clauses for identification of that although overall measures such as task duration and total stance-taking. However, our focus is on stance-taking in spo- word count do not indicate consistent differences across tasks, ken interactions, which involve short, fragmentary, or disfluent speakers do display significant differences in speaking style. utterances. Importantly, conversational speech harnesses infor- Factors such as increases in speaking rate, turn length, and dis- mation beyond its textual content to convey information about fluencies from weak- to strong-stance tasks are consistent with stance, for example through intonation, speaking rate, stress, increased involvement by the participants and provide evidence and precision of articulation [3, 4]. In general, issues of sub- in support of the experimental design. jectivity, sentiment, and stance in speech have received much Index Terms: Stance-taking, involvement, conversation speech less attention, and this work has primarily relied on existing corpus, speaking style conversational dyadic ([9] in [10]) or multi-party meeting data ([11, 12, 13] in [14, 15, 16], respectively). In these cases, a 1. Introduction small portion of the existing corpus was annotated for subjec- tivity or related factors such as agreement or arguing. Even Stance-taking is an essential component of interactive collab- using speech data, many of the approaches to automatic subjec- oration, negotiation, and decision-making. When people take tivity recognition have relied primarily on word or n-gram con- stances, they attempt to convey subjective feelings, attitudes, tent [17], and their efforts to incorporate prosodic information or opinions about the topic they are discussing [1, 2]. This yielded no significant improvement [18]. However, [15] found can involve several levels of linguistic information, including that access to the additional information in the audio channel of acoustic, prosodic, lexical, and pragmatic factors. In theo- meeting recordings enabled annotators to better identify opin- retical linguistics, descriptions of stance have been generally ions, especially negative opinions, than using transcripts alone. 1.1. Motivating a corpus of stance-taking in conversation A: My clothing items are at the bottom of th- of the third column. .. So, I have things like jackets, shoelaces, socks, The ATAROS corpus is designed specifically for the purpose vests, coats, sweaters, boots, hats. [...] of identifying acoustically-measurable signals of stance-taking Boots, hats, backpacks, um - .. Although, backpacks, I would in natural speech, and as such, it provides several advantages put that in with the camping supplies. over speech collected for other purposes. Limitations of exist- ing corpora include issues with recording quality, speaker at- Table 1: Snippet from Map Task designed to elicit stance- tributes, and the type and content of speech. Recording quality neutral first-mentions. varies widely when audio is gathered from sources not created for linguistic analysis. Common concerns are recording condi- tions and microphone type and placement, which often affect There is a natural tension between the use of a carefully the signal-to-noise ratio and acoustic intensity. For example, controlled dataset for analysis and the desire to apply such anal- if the distance between speaker and microphone varies unpre- ysis to the growing wealth of naturally occurring spoken lan- dictably, intensity is an unreliable measure, just as it is when guage. The former allows greater control and power, bringing loudness is adjusted for public broadcast (TV, radio, etc.). subtle contrasts into sharp relief. The latter allows evaluation on More specific to the study of linguistic variation is the abil- the types of data in real-world applications, where such controls ity to disentangle within- and between-speaker variation. Fac- are impossible. Thus we plan to collect a well-controlled corpus tors to consider include speaker demographics, social roles, and of stance-taking to fully explore the range of associated linguis- the amount and type of speech collected from each person. So- tic behaviors and then to generalize these findings to apply to cial factors such as age, gender, ethnicity, dialect region, and the a larger-scale, high stakes, naturalistic corpus: Congressional relationship between speaker and audience commonly correlate Hearings of the Financial Crisis Inquiry Commission. with linguistic variation [19, 20, 21], but these attributes are The rest of the paper is organized as follows. In Section 2, not always known or controlled in audio collections. This was a we describe our corpus collection and transcription procedures, problem in the precursor to the ATAROS project [3, 4], in which including details on the design of the two collaborative tasks the political talk show under analysis contained only males, for which we compare descriptive measures of speaking style each from a different dialect region (which was only possible to in Section 3. Finally, Section 4 summarizes our current findings determine because they happened to be reasonably well-known and our plans for stance annotation. people with publically-available biographic information). The type of speech also matters; of interest here is stance in sponta- neous, unscripted, naturalistic conversation, which differs from 2. Corpus Design, Collection, Transcription read or performed speech in ways that may affect stance-taking. In this section, we present the tasks designed to elicit varying For example, the personal motives underlying stance moves degrees of stance-taking and involvement, the data collection may differ greatly between social roles (boss, friend, parent, process, and transcription and alignment methodology. etc.) and between settings (meetings, public discussion, per- sonal conversation, etc.). More to the point, many situations 2.1. Task Design do not naturally involve a high density of the phenomenon un- der investigation. This is particularly relevant for stance-taking, Each dyad completes five collaborative problem-solving tasks which might be found in high densities in more formal, scripted designed to elicit frequent changes in stance and differing lev- situations such as debates but less reliably in conversation. Fi- els of involvement with the task. There are two groups of tasks, nally, when intra-speaker variation is desired, a larger amount each of which uses a set of about 50 target items chosen to rep- of speech is required from each speaker in each condition pre- resent the main vowel categories of Western American English dicted to have an effect, in order to obtain enough power for in fairly neutral consonantal
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