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Turns Analysis for Automatic Role Recognition

  • Sarah Favre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8045)

Abstract

This article presents approaches for recognizing automatically the roles people play in a wide range of interaction settings. 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).

Keywords

Automatic Speech Recognition Lexical Feature Interaction Setting Lexical Choice Market Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sarah Favre
    • 1
    • 2
  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.Ecole Polytechnique Federale de LausanneLausanneSwitzerland

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