Examining the Relationship Between Speech Perception, Production Distinctness, and Production Variability Article Swipe
Hung-Shao Cheng
,
Caroline A. Niziolek
,
Adam Buchwald
,
Tara McAllister
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fnhum.2021.660948
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fnhum.2021.660948
Several studies have demonstrated that individuals’ ability to perceive a speech sound contrast is related to the production of that contrast in their native language. The theoretical account for this relationship is that speech perception and production have a shared multimodal representation in relevant sensory spaces (e.g., auditory and somatosensory domains). This gives rise to a prediction that individuals with more narrowly defined targets will produce greater separation between contrasting sounds, as well as lower variability in the production of each sound. However, empirical studies that tested this hypothesis, particularly with regard to variability, have reported mixed outcomes. The current study investigates the relationship between perceptual ability and production ability, focusing on the auditory domain. We examined whether individuals’ categorical labeling consistency for the American English /ε/–/æ/ contrast, measured using a perceptual identification task, is related to distance between the centroids of vowel categories in acoustic space (i.e., vowel contrast distance) and to two measures of production variability: the overall distribution of repeated tokens for the vowels (i.e., area of the ellipse) and the proportional within-trial decrease in variability as defined as the magnitude of self-correction to the initial acoustic variation of each token (i.e., centering ratio). No significant associations were found between categorical labeling consistency and vowel contrast distance, between categorical labeling consistency and area of the ellipse, or between categorical labeling consistency and centering ratio. These null results suggest that the perception-production relation may not be as robust as suggested by a widely adopted theoretical framing in terms of the size of auditory target regions. However, the present results may also be attributable to choices in implementation (e.g., the use of model talkers instead of continua derived from the participants’ own productions) that should be subject to further investigation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnhum.2021.660948
- https://www.frontiersin.org/articles/10.3389/fnhum.2021.660948/pdf
- OA Status
- gold
- Cited By
- 15
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3169393754
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3169393754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fnhum.2021.660948Digital Object Identifier
- Title
-
Examining the Relationship Between Speech Perception, Production Distinctness, and Production VariabilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-28Full publication date if available
- Authors
-
Hung-Shao Cheng, Caroline A. Niziolek, Adam Buchwald, Tara McAllisterList of authors in order
- Landing page
-
https://doi.org/10.3389/fnhum.2021.660948Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnhum.2021.660948/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnhum.2021.660948/pdfDirect OA link when available
- Concepts
-
Speech production, Vowel, Perception, Psychology, Categorical perception, Contrast (vision), Categorical variable, Consistency (knowledge bases), Speech perception, Cognitive psychology, Categorization, Mathematics, Speech recognition, Computer science, Statistics, Artificial intelligence, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5, 2023: 3, 2022: 3Per-year citation counts (last 5 years)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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