Skip to main navigation Skip to search Skip to main content

Model-based prosodic analysis of charismatic speech

  • Beuth University of Applied Sciences Berlin

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

593 Downloads (Pure)

Abstract

This study examines at a new level of quantitative detail the intonation and timing properties of charismatic speech by comparing two popular CEOs, Steve Jobs and Mark Zuckerberg, who are known from informal observations and formal perception experiments alike to be more or less charismatic speakers, respectively. By applying the Fujisaki model we decomposed F0 contours into baseline frequency, phrasal F0 excursions and pitch accent-associated F0 excursions. Timing details are examined by applying Pfitzinger’s model of perceived local speech rate to phone and syllable segmentations. Results suggest that high pitch not only involves generally higher F0 levels, but that these increases in F0 are not the same for every prosodic domain or level of the Fujisaki model. In addition we found significant differences depending on whether customers or investors are addressed.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Speech Prosody 2018
EditorsKatarzyna Klessa, Jolanta Bachan, Agnieszka Wagner, Maciej Karpiński, Daniel Śledziński
PublisherInternational Speech Communication Association
Publication date2018
Pages814-818
DOIs
Publication statusPublished - 2018
Event9th International Conference on Speech Prosody 2018 - Poznan, Poland
Duration: 13. Jun 2018 → 16. Jun 2018

Conference

Conference9th International Conference on Speech Prosody 2018
Country/TerritoryPoland
CityPoznan
Period13/06/2018 → 16/06/2018

Keywords

  • Charisma
  • F0
  • Speech rate
  • Fujisaki model.
  • Steve Jobs
  • Mark Zuckerberg
  • Prosody
  • Intonation
  • Fujisaki model

Fingerprint

Dive into the research topics of 'Model-based prosodic analysis of charismatic speech'. Together they form a unique fingerprint.

Cite this