Naïve listeners' prominence and boundary perception

Yoonsook Mo, Jennifer Cole, Eun Kyung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper examines how ordinary listeners, naïve with respect to the phonetics and phonology of prosody, perceive the location of prosodic boundaries that demarcate speech "chunks" and prominences that serve a "highlighting" function, in spontaneous speech (Buckeye corpus). Over 70 naïve listeners marked the locations of prominences and boundaries in a real-time transcription task. Fleiss' multitranscribers' reliability tests show that naïve transcribers are consistent in their perception of prosodic boundaries and prominences. Specifically, we observe higher multi-transcriber agreement scores for boundary marking than for prominence marking. Variation between transcriptions of the same speech excerpt produced by different listeners reveals individual differences in the perception of prominences and boundaries. Variation in Fleiss' multi-transcribers' agreement scores for excerpts from different speakers suggests that speakers vary in how they structure an utterance prosodically and/or in how effectively they cue prosodic structure. We also find that nuclear prominences are more consistently perceived by naïve listeners than prenuclear prominences. The finding that naïve listeners agree well above chance on the location of prosodic events indicates that naïve transcription is a valid method for prosody analysis which can augment analysis based solely on expert labeling.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th International Conference on Speech Prosody, SP 2008
PublisherInternational Speech Communications Association
Number of pages4
ISBN (Print)9780616220030
StatePublished - Jan 1 2008
Event4th International Conference on Speech Prosody 2008, SP 2008 - Campinas, Brazil
Duration: May 6 2008May 9 2008


Other4th International Conference on Speech Prosody 2008, SP 2008

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software
  • Mechanical Engineering


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