Interpersonal stance in police interviews: content analysis

A serious game for learning the social skills required for effective police interviewing is a challenging idea. Building artificial conversational characters that play the role of a suspect in a police interrogation game requires computational models of police interviews as well as of the internal psychological mechanisms that determine the behaviour of suspects in this special type of dialogues. Leary's interactional circumplex is used in police interview training as a theoretical framework to understand how suspects take stance during an interview and how this is related to the stance and the strategy that the interviewer takes. Interactional stance is a fuzzy notion. The question that we consider here is whether different observers of police nterviews agree on the type of stance that suspect and policemen take and express in a face-to-face interview. We analyzed police interviews and report about a stance annotation exercise. We conclude that although inter-annotator agreement on stance labeling on the level of speech segments is low, a majority voting meta-annotator" is able to reveal the important dynamics in stance taking in a police interview. Then we explore the relation between the stance taken by the suspect and turn-taking behaviour, overlaps, interruptions, pauses and silences. Our findings contribute to building computational models of non-player characters that allow more natural turn-taking behaviour in serious games\ud instead of the one-at-a-time regime in interview training games

A Computational Model of Non-Cooperation in Natural Language Dialogue

A common assumption in the study of conversation is that participants fully cooperate in order to maximise the effectiveness of the exchange and ensure communication flow. This assumption persists even in situations in which the private goals of the participants are at odds: they may act strategically pursuing their agendas, but will still adhere to a number of linguistic norms or conventions which are implicitly accepted by a community of language users. However, in naturally occurring dialogue participants often depart from such norms, for instance, by asking inappropriate questions, by avoiding to provide adequate answers or by volunteering information that is not relevant to the conversation. These are examples of what we call linguistic non-cooperation. This thesis presents a systematic investigation of linguistic non-cooperation in dialogue. Given a specific activity, in a specific cultural context and time, the method proceeds by making explicit which linguistic behaviours are appropriate. This results in a set of rules: the global dialogue game. Non-cooperation is then measured as instances in which the actions of the participants are not in accordance with these rules. The dialogue game is formally defined in terms of discourse obligations. These are actions that participants are expected to perform at a given point in the dialogue based on the dialogue history. In this context, non-cooperation amounts to participants failing to act according to their obligations. We propose a general definition of linguistic non-cooperation and give a specific instance for political interview dialogues. Based on the latter, we present an empirical method which involves a coding scheme for the manual annotation of interview transcripts. The degree to which each participant cooperates is automatically determined by contrasting the annotated transcripts with the rules in the dialogue game for political interviews. The approach is evaluated on a corpus of broadcast political interviews and tested for correlation with human judgement on the same corpus. Further, we describe a model of conversational agents that incorporates the concepts and mechanisms above as part of their dialogue manager. This allows for the generation of conversations in which the agents exhibit varying degrees of cooperation by controlling how often they favour their private goals instead of discharging their discourse obligations

Interpersonal stance in police interviews: content analysis

A serious game for learning the social skills required for effective police interviewing is a challenging idea. Building artificial conversational characters that play the role of a suspect in a police interrogation game requires computational models of police interviews as well as of the internal psychological mechanisms that determine the behaviour of suspects in this special type of dialogues. Leary's interactional circumplex is used in police interview training as a theoretical framework to understand how suspects take stance during an interview and how this is related to the stance and the strategy that the interviewer takes. Interactional stance is a fuzzy notion. The question that we consider here is whether different observers of police nterviews agree on the type of stance that suspect and policemen take and express in a face-to-face interview. We analyzed police interviews and report about a stance annotation exercise. We conclude that although inter-annotator agreement on stance labeling on the level of speech segments is low, a majority voting meta-annotator" is able to reveal the important dynamics in stance taking in a police interview. Then we explore the relation between the stance taken by the suspect and turn-taking behaviour, overlaps, interruptions, pauses and silences. Our findings contribute to building computational models of non-player characters that allow more natural turn-taking behaviour in serious games instead of the one-at-a-time regime in interview training games

Spot the conversation: speaker diarisation in the wild

The goal of this paper is speaker diarisation of videos collected 'in the wild'. We make three key contributions. First, we propose an automatic audio-visual diarisation method for YouTube videos. Our method consists of active speaker detection using audio-visual methods and speaker verification using self-enrolled speaker models. Second, we integrate our method into a semi-automatic dataset creation pipeline which significantly reduces the number of hours required to annotate videos with diarisation labels. Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community. Our dataset consists of overlapping speech, a large and diverse speaker pool, and challenging background conditions.Comment: The dataset will be available for download from http://www.robots.ox.ac.uk/~vgg/data/voxceleb/voxconverse.html . The development set will be released in July 2020, and the test set will be released in October 202

Overlap as Conversational Strategies in an American Daytime Talk Show “The View”

The present study aims at figuring out the tendency of participants in employing overlap as conversational strategies by notably identifying the resources that herald overlap types along with its features occurring in an American daytime talk show named The View.  The forty-eight minutes of the talk show aired on November 7, 2,019 was retrieved from the official ABC channel in the Youtube platform that has been videotaped. In analyzing and illustrating the data, the present study employed an approach of Conversation Analysis as an inductive-qualitative method. Data collected from hosts and guests’ utterances were transcribed and scrutinized to explore how overlaps are treated in the course of a talk show and parties’ strategies of turn holding and turn claiming. The findings revealed that parties in The View orient to use four types of overlap to deliver their perspective on issues being discussed. The classification of overlap types relied on the entailment of speaker change leading to whether it is intrusive or collaborative. Backchannels as a collaborative overlap were used as a high number of overlaps, followed by complementary types, anticipated turn-taking, and intrusive overlap of turn-request as the lowest number of overlaps.  In designing the turn, parties tended to use two strategies, namely direct and indirect

Lexical Retrieval Hypothesis in Multimodal Context

Multimodal corpora have become an essential language resource for language science and grounded natural language processing (NLP) systems due to the growing need to understand and interpret human communication across various channels. In this paper, we first present our efforts in building the first Multimodal Corpus for Languages in Taiwan (MultiMoco). Based on the corpus, we conduct a case study investigating the Lexical Retrieval Hypothesis (LRH), specifically examining whether the hand gestures co-occurring with speech constants facilitate lexical retrieval or serve other discourse functions. With detailed annotations on eight parliamentary interpellations in Taiwan Mandarin, we explore the co-occurrence between speech constants and non-verbal features (i.e., head movement, face movement, hand gesture, and function of hand gesture). Our findings suggest that while hand gestures do serve as facilitators for lexical retrieval in some cases, they also serve the purpose of information emphasis. This study highlights the potential of the MultiMoco Corpus to provide an important resource for in-depth analysis and further research in multimodal communication studies

The Pisa Audio-visual Corpus Project: A multimodal approach to ESP research and teaching

This paper presents an ongoing project sponsored by the University of Pisa Language Centre to compile an audiovisual corpus of specialized types of discourse of particular relevance to ESP learners in higher education. The first phase of the project focuses on collecting digitally available video clips that encode specialized language in a range of genres along an ‘authentic’ to ‘fictional’ continuum. The video clips will be analyzed from a multimodal perspective to determine how various semiotic resources work together to construct meaning. They will then be utilized in the ESP classroom to increase learners’ awareness of the key contribution of different modes in specialized communication. We present some exploratory multimodal analyses performed on video clips that encode instances of political discourse across two different genres on the extreme poles of the continuum: a fictional political drama film and an authentic political science lecture

Very Short Utterances in Conversation

Faced with the difficulties of finding an operationalized definition of backchannels, we have previously proposed an intermediate, auxiliary unit – the very short utterance (VSU) – which is defined operationally and is automatically extractable from recorded or ongoing dialogues. Here, we extend that work in the following ways: (1) we test the extent to which the VSU/NONVSU distinction corresponds to backchannels/non-backchannels in a different data set that is manually annotated for backchannels – the Columbia Games Corpus; (2) we examine to the extent to which VSUS capture other short utterances with a vocabulary similar to backchannels; (3) we propose a VSU method for better managing turn-taking and barge-ins in spoken dialogue systems based on detection of backchannels; and (4) we attempt to detect backchannels with better precision by training a backchannel classifier using durations and inter-speaker relative loudness differences as features. The results show that VSUS indeed capture a large proportion of backchannels – large enough that VSUs can be used to improve spoken dialogue system turntaking; and that building a reliable backchannel classifier working in real time is feasible