000002357 001__ 2357
000002357 005__ 20250424232727.0
000002357 0247_ $$2DOI$$a10.6083/M4WH2N88
000002357 037__ $$aETD
000002357 245__ $$aRun-time information fusion in large vocabulary continuous speech recognition
000002357 260__ $$bOregon Health and Sciences University
000002357 269__ $$a2004-04-01
000002357 336__ $$aDissertation
000002357 502__ $$bPh.D.
000002357 520__ $$aContinuous speech recognition systems are environmentally sensitive and suffer from the great variability of speech. In order to achieve recognition robustness, there's a strong interest among researchers on how to fuse different information sources for speech recognition. A common problem of those approaches is that complementary information is lost either before or after recognition.  To  avoid this unrecoverable information loss, and to better utilize this complementary information, we  proposed a run time information fbsion scheme.  The hypothesis of this thesis is that by  performing fusion at  different  levels and stages of a Large Vocabulary Continuous Speech Recognition  (LVCSR)  system,  especially inside the  decoder,  more reliable and efficient fusion is possible.
000002357 542__ $$fIn copyright - single owner
000002357 650__ $$aSpeech Perception$$026233
000002357 650__ $$aSpeech Recognition Software$$036177
000002357 691__ $$aOGI School of Science and Engineering$$041365
000002357 692__ $$aOGI Department of Computer Science and Engineering$$041405
000002357 7001_ $$aZheng, Chengyi$$uOregon Health and Science University$$041354
000002357 7201_ $$aYonghong, Yan$$uOregon Health and Science University$$041354$$7Personal$$eAdvisor
000002357 8564_ $$96eb803ad-f9a1-4418-8e6a-aa324bbbca9d$$s6830304$$uhttps://digitalcollections.ohsu.edu/record/2357/files/3089_etd.pdf$$ePublic$$275184a12b22240dc7d93eb314a28331a$$31
000002357 905__ $$a/rest/prod/xw/42/n8/11/xw42n811v
000002357 909CO $$ooai:digitalcollections.ohsu.edu:2357$$pstudent-work
000002357 980__ $$aTheses and Dissertations