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1
Using acoustic distance and acoustic absement to quantify lexical competition ...
Matthew C. Kelley; Benjamin V. Tucker. - : University of Alberta Library, 2022
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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
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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8
How do words compete? Quantifying lexical competition with acoustic distance
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9
A comparison of four vowel overlap measures
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10
MALD MFCC subset
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11
Assessing head-and-neck cancer patient speech with the vowel dispersion index
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12
Massive auditory lexical decision: Investigating performance in noisy environments
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13
Measuring the dispersion of density in head and neck cancer patients' vowel spaces: The vowel dispersion index
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14
Supplementary files for "A Comparison of Four Vowel Overlap Measures"
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15
A comparison of four vowel overlap measures
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16
Using acoustic distance to quantify lexical competition
Abstract: This paper has been significantly updated and published in The Journal of the Acoustical Society of America. Please read and cite that version instead, which can be found at https://doi.org/10.1121/10.0009584. The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance, rather than phonological neighborhood density. The indication of a word's lexical competition is given by what is termed its acoustic distinctiveness, which is taken as its average acoustic distance to all other 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 phonological neigbhorhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density, regardless of which kind of acoustic representation is used. Acoustic distinctiveness does not seem to explain all the same things as phonological neighborhood density, however. The different areas that these two predictors explain are discussed, in addition to potential theoretical implications of acoustic distinctiveness's usefulness in models. The paper concludes with motiviations for why a researcher may want to use acoustic disinctiveness over phonological neighborhood density in future experiments. This document was prepared as part of a generals paper course in the fall 2018 term.
Keyword: acoustic distance; dynamic time warping; lexical competition; linguistics; mental lexicon; phonetics; phonological neighborhood density; psycholinguistics
URL: https://doi.org/10.7939/r3-wbhs-kr84
https://era.library.ualberta.ca/items/69e94fb3-1860-4ad5-a487-b7a8db1fde98
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17
How acoustic distinctiveness affects spoken word recognition: A pilot study
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18
Recognition of spoken pseudowords
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19
How do we recognize pseudowords in an audio signal?
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20
A comparison of four vowel overlap metrics
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