Segmentation of Highly Vocalic Speech Via Statistical Learning: Initial Results From Danish, Norwegian, and English. Issue 1 (11th October 2018)

• Record Type: Journal Article
• Authors: Trecca, Fabio; McCauley, Stewart M.; Andersen, Sofie Riis; Bleses, Dorthe; Basbøll, Hans; Højen, Anders; Madsen, Thomas O.; Ribu, Ingeborg Sophie Bjønness; Christiansen, Morten H.
• Is Part Of: Language learning. Volume 69:Issue 1(2019)
• Page Start: 143
• Page End: 176
• Publication Date: 2018-10-11
• Subjects: statistical learning -- word segmentation -- vowels -- Danish -- crosslinguistic -- language distance; Language and languages -- Periodicals; Language and languages -- Study and teaching -- Periodicals; Linguistics; 407
• DOI: 10.1111/lang.12325
• Languages: English
• ISSNs: 0023-8333

Abstract

Abstract : Research has shown that contoids (phonetically defined consonants) may provide more robust and reliable cues to syllable and word boundaries than vocoids (phonetically defined vowels). Recent studies of Danish, a language characterized by frequent long sequences of vocoids in speech, have suggested that the reduced occurrence of contoids may make speech in it intrinsically harder to segment than in closely related languages such as Norwegian. We addressed this hypothesis empirically in an artificial language learning experiment with native speakers of Danish, Norwegian, and English. We tested whether artificial speech consisting of concatenated contoid–vocoid syllables is easier to segment than speech consisting of vocoid–vocoid syllables where the first segment is a semivowel and the second a full vowel. Contrary to what was expected, we found no effect of the phonetic makeup of the syllables on speech segmentability. Possible interpretations and implications of this result are discussed. Open Practices: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework athttps://osf.io/rmjtg . Learn more about the Open Practices badges from the Center for Open Science:https://osf.io/tvyxz/wiki . Abstract :