Models of Human Phone Transcription in Noise Based on Intelligibility Predictors
Lobdell, Bryce E.
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https://hdl.handle.net/2142/81127
Description
Title
Models of Human Phone Transcription in Noise Based on Intelligibility Predictors
Author(s)
Lobdell, Bryce E.
Issue Date
2009
Doctoral Committee Chair(s)
Hasegawa-Johnson, Mark A.
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Speech Communication
Language
eng
Abstract
The key findings of the experiments are the following: (1) the Articulation Index model recognition accuracy works very well in some phonetic contexts and fails in others, (2) the Articulation Index model is the average of a number of more specific models with their own parameters, (3) audibility of speech does not explain all variation but explains a great deal of it, and (4) phonetic importance is not spread uniformly over the time and frequency. We speculate that humans may use different representations of speech, depending on the phonetic context, and we suggest experiments controlling frequency-band specific signal-to-noise ratio and level to resolve these issues.
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