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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
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17
How acoustic distinctiveness affects spoken word recognition: A pilot study
Abstract: In the present study, I propose an acoustically-based alternative to phonological neighborhood density. Phonological neighborhood density has been used in many studies as an approximate quantification of lexical competition during spoken word recognition. However, phonological neighborhood density relies on phonemes, which are divorced from acoustic data, and the method for calculating phonological neighborhood density assigns equal weight to each potential kind of change in the acoustic signal. Moving the quantification of lexical competition into the acoustic domain can mitigate these shortcomings of phonological neighborhood density. I discuss a method of quantifying competition with acoustics, and for a given word, I refer to the result calculating this measure as the word's "acoustic distinctiveness." I then show how this acoustic distinctiveness measure outperforms phonological neighborhood density in modeling participant response times in an auditory lexical decision task. Finally, I conclude with a post-hoc comparison of acoustic distinctiveness and phonological neighborhood density, and then discuss what implications these results have for the mental lexicon. This talk was presented at the 11th International Conference on the Mental Lexicon in Edmonton, AB. NB: If your slideshow presentation program will not play the audio files, they are also available here for you to download and listen to separately.
Keyword: acoustic distance; linguistics; phonetics; psycholinguistics; spoken word recognition
URL: https://era.library.ualberta.ca/items/ad85581d-2d7a-45f5-85d6-852b073267e8
https://doi.org/10.7939/R39G5GV9Q
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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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