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Using acoustic distance and acoustic absement to quantify lexical competition ...
Matthew C. Kelley; Benjamin V. Tucker. - : University of Alberta Library, 2022
Abstract: Using phonological neighborhood density has been a common method to quantify lexical competition. It is useful and convenient but has shortcomings that are worth reconsidering. The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance and acoustic absement rather than phonological neighborhood density. The indication of a word's lexical competition is given by what is termed to be its acoustic distinctiveness, which is taken as its average acoustic absement to all words in the lexicon. A variety of acoustic representations for items in the lexicon are analyzed. Statistical modeling shows that acoustic distinctiveness has a similar effect trend as that of phonological neighborhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density regardless of which kind of acoustic representation is used. However, acoustic distinctiveness does not seem to explain all of the same ...
URL: https://era.library.ualberta.ca/items/b989ee11-10b5-4512-b794-fd959e1e1053
https://dx.doi.org/10.7939/r3-fj0r-6j02
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2
The recognition of spoken pseudowords ...
Matthew C. Kelley; Benjamin V. Tucker. - : University of Alberta Library, 2022
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3
Using acoustic distance and acoustic absement to quantify lexical competition
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4
Perception and timing of acoustic distance
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5
APhL Aligner: A Neural Network Forced-Alignment System
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6
Acoustic absement files
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7
Variability of /ð/ and /θ/ in the Casual Speech of Native English Speakers
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8
How do words compete? Quantifying lexical competition with acoustic distance ...
Matthew C. Kelley; Benjamin V. Tucker. - : University of Alberta Libraries, 2020
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9
Discriminative lexicon simulations material
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10
DIANA simulations material
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11
How do words compete? Quantifying lexical competition with acoustic distance
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12
A comparison of four vowel overlap measures
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13
Computational modeling of an auditory lexical decision task using jTRACE
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14
Shorter phone duration facilitates isolated spoken word recognition
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15
Emotion and lexical effects in an auditory lexical decision task with vocal affect
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16
jTRACE/TISK simulations material
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17
Massive auditory lexical decision: Investigating performance in noisy environments
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18
Supplementary files for "A Comparison of Four Vowel Overlap Measures"
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19
A comparison of four vowel overlap measures
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20
Using acoustic distance to quantify lexical competition
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