Acoustic Discriminability of the Complex Phonation System in !Xóõ.


Journal

Phonetica
ISSN: 1423-0321
Titre abrégé: Phonetica
Pays: Germany
ID NLM: 0376424

Informations de publication

Date de publication:
2020
Historique:
received: 20 10 2017
accepted: 02 10 2018
pubmed: 11 2 2019
medline: 15 4 2021
entrez: 11 2 2019
Statut: ppublish

Résumé

Phonation types, or contrastive voice qualities, are minimally produced using complex movements of the vocal folds, but may additionally involve constriction in the supraglottal and pharyngeal cavities. These complex articulations in turn produce a multidimensional acoustic output that can be modeled in various ways. In this study, I investigate whether the psychoacoustic model of voice by Kreiman et al. (2014) succeeds at distinguishing six phonation types of !Xóõ. Linear discriminant analysis is performed using parameters from the model averaged over the entire vowel as well as for the first and final halves of the vowel. The results indicate very high classification accuracy for all phonation types. Measures averaged over the vowel's entire duration are closely correlated with the discriminant functions, suggesting that they are sufficient for distinguishing even dynamic phonation types. Measures from all classes of parameters are correlated with the linear discriminant functions; in particular, the "strident" vowels, which are harsh in quality, are characterized by their noise, changes in spectral tilt, decrease in voicing amplitude and frequency, and raising of the first formant. Despite the large number of contrasts and the time-varying characteristics of many of the phonation types, the phonation contrasts in !Xóõ remain well differentiated acoustically.

Identifiants

pubmed: 30739113
pii: 000494301
doi: 10.1159/000494301
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

131-160

Informations de copyright

© 2019 S. Karger AG, Basel.

Auteurs

Marc Garellek (M)

Department of Linguistics, University of California, San Diego, San Diego, California, USA, mgarellek@ucsd.edu.

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Classifications MeSH