The SSPNet-Mobile Corpus: from the detection of non-verbal cues to the inference of social behaviour during mobile phone conversations

Polychroniou, Anna (2014) The SSPNet-Mobile Corpus: from the detection of non-verbal cues to the inference of social behaviour during mobile phone conversations. PhD thesis, University of Glasgow.

Abstract

Mobile phones are one of the main channels of communication in contemporary society.
However, the effect of the mobile phone on both the process of and, also, the non-verbal
behaviours used during conversations mediated by this technology, remain poorly understood.
This thesis aims to investigate the role of the phone on the negotiation process as well as,
the automatic analysis of non-verbal behavioural cues during conversations using mobile
telephones, by following the Social Signal Processing approach. The work in this thesis
includes the collection of a corpus of 60 mobile phone conversations involving 120 subjects, development of methods for the detection of non-verbal behavioural events (laughter,
fillers, speech and silence) and the inference of characteristics influencing social interactions
(personality traits and conflict handling style) from speech and movements while using the
mobile telephone, as well as the analysis of several factors that influence the outcome of
decision-making processes while using mobile phones (gender, age, personality, conflict
handling style and caller versus receiver role).
The findings show that it is possible to recognise behavioural events at levels well above
chance level, by employing statistical language models, and that personality traits and conflict
handling styles can be partially recognised. Among the factors analysed, participant role
(caller versus receiver) was the most important in determining the outcome of negotiation
processes in the case of disagreement between parties. Finally, the corpus collected for the
experiments (the SSPNet-Mobile Corpus) has been used in an international benchmarking
campaign and constitutes a valuable resource for future research in Social Signal Processing
and more generally in the area of human-human communication.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Artificial intelligence, machine learning, non-verbal behavioural cues, social behaviour, personality traits, conflict handling style, negotiation, smartphones
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Vinciarelli, Dr Alessandro
• Date of Award: 2014
• Depositing User: Dr Anna Polychroniou
• Unique ID: glathesis:2014-5686
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 30 Oct 2014 09:36
• Last Modified: 30 Oct 2014 09:38
• URI: https://theses.gla.ac.uk/id/eprint/5686