Automatic Role Recognition in Multiparty Recordings: Using Social Affiliation Networks for Feature Extraction

Automatic analysis of social interactions attracts increasing attention in the multimedia community. This paper considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automatic approach for the recognition of roles in both production environment contexts (e.g., news and talk-shows) and spontaneous situations (e.g., meetings). The experiments are performed over roughly 90 hours of material (one of the largest databases used for role recognition in the literature) and show that the recognition effectiveness depends on how much the roles influence the behavior of people. Furthermore, this work proposes the first approach for modeling mutual dependences between roles and assesses its effect on role recognition performance.

Social Network Analysis for Automatic Role Recognition

Sarah Favre

The computing community has shown a significant interest for the analysis of social interactions in the last decade. Different aspects of social interactions have been studied such as dominance, emotions, conflicts, etc. However, the recognition of roles has been neglected whereas these are a key aspect of social interactions. In fact, sociologists have shown not only that people play roles each time they interact, but also that roles shape behavior and expectations of interacting participants. The aim of this thesis is to fill this gap by investigating the problem of automatic role recognition in a wide range of interaction settings, including production environments, e.g. news and talk-shows, and spontaneous exchanges, e.g. meetings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). The importance of roles has been assessed in an application scenario as well. In particular, the thesis shows that roles help to segment talk-shows into stories, i.e. time intervals during which a single topic is discussed, with satisfactory performance. The main contributions of this thesis are as follows: To the best of our knowledge, this is the first work where social network analysis is applied to automatic analysis of conversation recordings. This thesis provides the first quantitative measure of how much roles constrain conversations, and a large corpus of recordings annotated in terms of roles. The results of this work have been published in one journal paper, and in five conference articles.

EPFL2011

Automatic Role Recognition in Multiparty Recordings Using Social Networks and Probabilistic Sequential Models

Sarah Favre, Alessandro Vinciarelli

The automatic analysis of social interactions is attracting significant interest in the multimedia community. This work addresses one of the most important aspects of the problem, namely the recognition of roles in social exchanges. The proposed approach is based on Social Network Analysis, for the representation of individuals in terms of their interactions with others, and probabilistic sequential models, for the recognition of role sequences underlying the sequence of speakers in conversations. The experiments are performed over different kinds of data (around 90 hours of broadcast data and meetings), and show that the performance depends on how formal the roles are, i.e. on how much they constrain people behavior.

2009

Automatic social role recognition and its application in structuring multiparty interactions

Ashtosh Sapru

Automatic processing of multiparty interactions is a research domain with important applications in content browsing, summarization and information retrieval. In recent years, several works have been devoted to find regular patterns which speakers exhibit in a multiparty interaction also known as social roles. Most of the research in literature has generally focused on recognition of scenario specific formal roles. More recently, role coding schemes based on informal social roles have been proposed in literature, defining roles based on the behavior speakers have in the functioning of a small group interaction. Informal social roles represent a flexible classification scheme that can generalize across different scenarios of multiparty interaction. In this thesis, we focus on automatic recognition of informal social roles and exploit the influence of informal social roles on speaker behavior for structuring multiparty interactions. To model speaker behavior, we systematically explore various verbal and non verbal cues extracted from turn taking patterns, vocal expression and linguistic style. The influence of social roles on the behavior cues exhibited by a speaker is modeled using a discriminative approach based on conditional random fields. Experiments performed on several hours of meeting data reveal that classification using conditional random fields improves the role recognition performance. We demonstrate the effectiveness of our approach by evaluating it on previously unseen scenarios of multiparty interaction. Furthermore, we also consider whether formal roles and informal roles can be automatically predicted by the same verbal and nonverbal features. We exploit the influence of social roles on turn taking patterns to improve speaker diarization under distant microphone condition. Our work extends the Hidden Markov model (HMM)- Gaussian mixture model (GMM) speaker diarization system, and is based on jointly estimating both the speaker segmentation and social roles in an audio recording. We modify the minimum duration constraint in HMM-GMM diarization system by using role information to model the expected duration of speaker's turn. We also use social role n-grams as prior information to model speaker interaction patterns. Finally, we demonstrate the application of social roles for the problem of topic segmentation in meetings. We exploit our findings that social roles can dynamically change in conversations and use this information to predict topic changes in meetings. We also present an unsupervised method for topic segmentation which combines social roles and lexical cohesion. Experimental results show that social roles improve performance of both speaker diarization and topic segmentation.