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Voice and Emphasis in Arabic Coronal Stops: Evidence for Phonological Compensation

The current study investigates multiple acoustic cues–voice onset time (VOT), spectral center of gravity (SCG) of burst, pitch (F0), and frequencies of the first (F1) and second (F2) formants at vowel onset—associated with phonological contrasts of voicing and emphasis in production of Arabic corona...

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Detalles Bibliográficos
Autor principal: Kulikov, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185181/
https://www.ncbi.nlm.nih.gov/pubmed/33455538
http://dx.doi.org/10.1177/0023830920986821
Descripción
Sumario:The current study investigates multiple acoustic cues–voice onset time (VOT), spectral center of gravity (SCG) of burst, pitch (F0), and frequencies of the first (F1) and second (F2) formants at vowel onset—associated with phonological contrasts of voicing and emphasis in production of Arabic coronal stops. The analysis of the acoustic data collected from eight native speakers of the Qatari dialect showed that the three stops form three distinct modes on the VOT scale: [d] is (pre)voiced, voiceless [t] is aspirated, and emphatic [ṭ] is voiceless unaspirated. The contrast is also maintained in spectral cues. Each cue influences production of coronal stops while their relevance to phonological contrasts varies. VOT was most relevant for voicing, but F2 was mostly associated with emphasis. The perception experiment revealed that listeners were able to categorize ambiguous tokens correctly and compensate for phonological contrasts. The listeners’ results were used to evaluate three categorization models to predict the intended category of a coronal stop: a model with unweighted and unadjusted cues, a model with weighted cues compensating for phonetic context, and a model with weighted cues compensating for the voicing and emphasis contrasts. The findings suggest that the model with phonological compensation performed most similar to human listeners both in terms of accuracy rate and error pattern.