Speaker diarization of multi-party conversations using participants role information: political debates and professional meetings

Speaker Diarization aims at inferring who spoke when in an audio stream and involves two simultaneous unsupervised tasks: (1) the estimation of the number of speakers, and (2) the association of speech segments to each speaker. Most of the recent efforts in the domain have addressed the problem using machine learning techniques or statistical methods (for a review see [11]) ignoring the fact that the data consists of instances of human conversations

The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings

Meeting analysis has a long theoretical tradition in social psychology, with established practical rami?cations in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat arti?cial conditions. This paper presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesised that the conversational structure of these meetings, as indicated by sequencing and duration of vocalisations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented which determines the optimal length of vocalisation sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classi?cation as a segmentation method, and its applicability to meeting segmentation in other domains are discusse

Automatic Speaker Role Labeling in AMI Meetings: Recognition of Formal and Social Roles

This work aims at investigating the automatic recognition of speaker role in meeting conversations from the AMI corpus. Two types of roles are considered: formal roles, fixed over the meeting duration and recognized at recording level, and social roles related to the way participants interact between themselves, recognized at speaker turn level. Various structural, lexical and prosodic features as well as Dialog Act tags are exhaustively investigated and combined for this purpose. Results reveal an accuracy of 74% in recognizing the speakers formal roles and an accuracy of 66% (percentage of time) in correctly labeling the social roles. Feature analysis reveals that lexical features provide the higher performances in formal/functional role recognition while prosodic features provide the higher performances in social role recognition. Furthermore results reveal that social role recognition in case of rare roles in the corpus can be improved through the use of lexical and Dialog Act information combined over short time windows

Turn-Taking Strategies Produced by Male and Female Presenters in American TV Shows

This study aims at examining the turn-taking strategies performed by male presenter, Jimmy Kimmel, and female presenter, Ellen DeGeneres, in two American TV talk shows. The data were analyzed using Stenstrom theory (1994) for the turn-taking strategies performed by both presenters. The findings revealed the following. (1) As the representation of male presenters, in conversation with male guests, Jimmy performed most of the strategies such as hesitant start, clean start, uptakes, links, alert, filled pause/ verbal fillers, silent pause, lexical repetition, a new start prompting and appealing. However, he did not apply metacomment and giving up strategy. Furthermore, in conversation with female guests, he used all the strategies, except hesitant start, metacomment, lexical repetition, a new start, and giving up strategy. (2) As the representation of female presenters, Ellen did not use metacomment, silent pause, and giving up strategies in her conversation with male guests. On the other hand, in conversation with female guests, she used all the strategies, except metacomment strategy. (3) This study also revealed that male presenters interrupted more often to female guests than to male guests, which supports the theory proposed by Zimmerman and West (1975)