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oai:www.ideals.illinois.edu:2142/81127 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Hasegawa-Johnson, Mark A. Lobdell, Bryce E. 2015-09-25T20:09:41Z 2015-09-25T20:09:41Z 10000-01-01 2009 2009 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. Made available in DSpace on 2015-09-25T20:09:41Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3363022.pdf: 8260631 bytes, checksum: c0eabe81e36b2ad4fb704e142c6b098b (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 82409 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 174 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/81127 (MiAaPQ)AAI3363022 eng Speech Communication Models of Human Phone Transcription in Noise Based on Intelligibility Predictors text Electrical and Computer Engineering Electrical and Computer Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.