A longitudinal database of Irish political speech with annotations of speaker ability.

This paper presents the Irish Political Speech Database, an English-language database collected from Irish political recordings. The database is collected with automated indexing and content retrieval in mind, and thus is gathered from real-world recordings (such as television interviews and electio...

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Published in:Language Resources & Evaluation Vol. 52; no. 2; pp. 401 - 433
Main Authors: Cullen, Ailbhe, Harte, Naomi
Format: Article
Published: Springer Nature Jun2018
Subjects:
Online Access:View this record in EBSCOhost
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        atl: A longitudinal database of Irish political speech with annotations of speaker ability.
      aug:
        au:
          Cullen, Ailbhe
          Harte, Naomi
        affil: Sigmedia, ADAPT Centre, School of Engineering, Trinity College, Dublin, Ireland
      su:
        Political oratory
        English language
        Paralinguistics
        Affective computing
        Machine learning
      sug:
        subj:
          Political oratory
          English language
          Paralinguistics
          Affective computing
          Machine learning
      keyword:
        Charisma
        Computational paralinguistics
        Political speech
        Speaker ability
      ab: This paper presents the Irish Political Speech Database, an English-language database collected from Irish political recordings. The database is collected with automated indexing and content retrieval in mind, and thus is gathered from real-world recordings (such as television interviews and election rallies) which represent the nature and quality of recordings which will be encountered in practical applications. The database is labelled for six speaker attributes: boring; charismatic; enthusiastic; inspiring; likeable; and persuasive. Each of these traits is linked to the perceived ability or appeal of the speaker, and as such are relevant to a range of content retrieval and speech analysis tasks. The six base attributes are combined to form a metric of Overall Speaker Appeal. A set of baseline experiments is presented, which demonstrate the potential of this database for affective computing studies. Classification accuracies of up to 76% are achieved, with little feature or system optimisation.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2018. All Rights Reserved.
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