31,487 research outputs found
Extraction and Analysis of Dynamic Conversational Networks from TV Series
Identifying and characterizing the dynamics of modern tv series subplots is
an open problem. One way is to study the underlying social network of
interactions between the characters. Standard dynamic network extraction
methods rely on temporal integration, either over the whole considered period,
or as a sequence of several time-slices. However, they turn out to be
inappropriate in the case of tv series, because the scenes shown onscreen
alternatively focus on parallel storylines, and do not necessarily respect a
traditional chronology. In this article, we introduce Narrative Smoothing, a
novel network extraction method taking advantage of the plot properties to
solve some of their limitations. We apply our method to a corpus of 3 popular
series, and compare it to both standard approaches. Narrative smoothing leads
to more relevant observations when it comes to the characterization of the
protagonists and their relationships, confirming its appropriateness to model
the intertwined storylines constituting the plots.Comment: arXiv admin note: substantial text overlap with arXiv:1602.0781
A Generative Model of Group Conversation
Conversations with non-player characters (NPCs) in games are typically
confined to dialogue between a human player and a virtual agent, where the
conversation is initiated and controlled by the player. To create richer, more
believable environments for players, we need conversational behavior to reflect
initiative on the part of the NPCs, including conversations that include
multiple NPCs who interact with one another as well as the player. We describe
a generative computational model of group conversation between agents, an
abstract simulation of discussion in a small group setting. We define
conversational interactions in terms of rules for turn taking and interruption,
as well as belief change, sentiment change, and emotional response, all of
which are dependent on agent personality, context, and relationships. We
evaluate our model using a parameterized expressive range analysis, observing
correlations between simulation parameters and features of the resulting
conversations. This analysis confirms, for example, that character
personalities will predict how often they speak, and that heterogeneous groups
of characters will generate more belief change.Comment: Accepted submission for the Workshop on Non-Player Characters and
Social Believability in Games at FDG 201
The roundtable: an abstract model of conversation dynamics
Is it possible to abstract a formal mechanism originating schisms and
governing the size evolution of social conversations? In this work a
constructive solution to such problem is proposed: an abstract model of a
generic N-party turn-taking conversation. The model develops from simple yet
realistic assumptions derived from experimental evidence, abstracts from
conversation content and semantics while including topological information, and
is driven by stochastic dynamics. We find that a single mechanism - namely the
dynamics of conversational party's individual fitness, as related to
conversation size - controls the development of the self-organized schisming
phenomenon. Potential generalizations of the model - including individual
traits and preferences, memory effects and more elaborated conversational
topologies - may find important applications also in other fields of research,
where dynamically-interacting and networked agents play a fundamental role.Comment: 18 pages, 4 figures, to be published in Journal of Artificial
Societies and Social Simulatio
A Voice is Worth a Thousand Words: The Implications of the Micro-Coding of Social Signals in Speech for Trust Research
While self-report measures are often highly reliable for field research on trust (Mayer and Davis, 1999), subjects often cannot complete surveys during real time interactions. In contrast, the social signals that are embedded in the non-linguistic elements of conversations can be captured in real time and extracted with the assistance of computer coding. This chapter seeks to understand how computer-coded social signals are related to interpersonal trust
Getting to the Core of Role: Defining Interpreters' Role Space
This article describes a new model of interpreted interactions that will help students as well as experienced
practitioners define and delineate the decisions that they make. By understanding the dimensions that comprise the
concept we call role, interpreters can more effectively allow participants to have successful communicative interactions
An End-to-End Conversational Style Matching Agent
We present an end-to-end voice-based conversational agent that is able to
engage in naturalistic multi-turn dialogue and align with the interlocutor's
conversational style. The system uses a series of deep neural network
components for speech recognition, dialogue generation, prosodic analysis and
speech synthesis to generate language and prosodic expression with qualities
that match those of the user. We conducted a user study (N=30) in which
participants talked with the agent for 15 to 20 minutes, resulting in over 8
hours of natural interaction data. Users with high consideration conversational
styles reported the agent to be more trustworthy when it matched their
conversational style. Whereas, users with high involvement conversational
styles were indifferent. Finally, we provide design guidelines for multi-turn
dialogue interactions using conversational style adaptation
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
On combining the facial movements of a talking head
We present work on Obie, an embodied conversational
agent framework. An embodied conversational agent, or
talking head, consists of three main components. The
graphical part consists of a face model and a facial muscle
model. Besides the graphical part, we have implemented
an emotion model and a mapping from emotions to facial
expressions. The animation part of the framework focuses
on the combination of different facial movements
temporally. In this paper we propose a scheme of
combining facial movements on a 3D talking head
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