000002787 001__ 2787
000002787 005__ 20251203131209.0
000002787 0247_ $$2DOI$$a10.6083/M4X34W66
000002787 037__ $$aETD
000002787 245__ $$aAlgorithms for extracting robust and accurate speech features and their application in clinical domain
000002787 269__ $$a2014
000002787 336__ $$aDissertation
000002787 502__ $$bPh.D.
000002787 502__ $$gComputer Science & Electrical Engineering (sunsetting)
000002787 520__ $$aSamples of everyday conversations are being collected and analyzed in a growing num- ber of applications, ranging from studying behavior in social psychology to clinical assess- ment of voice pathology and even cognitive function. Aside from the spoken words, the acoustic properties of speech samples can provide important cues in these applications. The goal of this study is developing novel algorithms for robust and accurate estimation of speech features and employing them to build probabilistic speech models for character- izing and analyzing clinical speech. We aim to achieve accurate and reliable estimation of voiced segments, fundamental frequency, harmonic-to-noise ratio (HNR), jitter, and shim- mer for clinical speech analysis. Towards this goal, we adopt a harmonic model (HM) of speech.
000002787 540__ $$fCC BY
000002787 542__ $$fIn copyright - single owner
000002787 650__ $$aDepression$$017614
000002787 650__ $$aParkinson Disease$$023617
000002787 650__ $$aSpeech$$026226
000002787 650__ $$aAlgorithms$$014437
000002787 650__ $$aCognition$$016868
000002787 6531_ $$aspeech processing systems
000002787 691__ $$aSchool of Medicine$$041369
000002787 692__ $$aCenter for Spoken Language Understanding$$041388
000002787 7001_ $$aAsgari, Meysam$$uOregon Health and Science University$$041354
000002787 7201_ $$aShafran, Izhak$$uGoogle, Inc.$$7Personal$$eAdvisor
000002787 8564_ $$947dbe837-8bde-42e0-adcd-841ad6105ae6$$s960418$$uhttps://digitalcollections.ohsu.edu/record/2787/files/3555_etd.pdf$$ePublic$$2bde289f97f4a82207b32022f219534bd$$31
000002787 905__ $$a/rest/prod/h7/02/q6/61/h702q661v
000002787 909CO $$ooai:digitalcollections.ohsu.edu:2787$$pstudent-work
000002787 980__ $$aTheses and Dissertations