Objective Intelligibility Assessment by Automated Segmental and Suprasegmental Listening Error Analysis.
Purpose: Subjective speech intelligibility assessment is often preferred over more objective approaches that rely on transcript scoring. This is, in part, because of the intensive manual labor associated with extracting objective metrics from transcribed speech. In this study, we propose an automate...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 62; no. 9; pp. 3359 - 3367 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Sep2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=138842095&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 138842095 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Sep2019 vid: 62 iid: 9 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 138842095 10.1044/2019_JSLHR-S-19-0119 ppf: 3359 ppct: 8 formats: fmt: @attributes: type: P size: 509KB tig: atl: Objective Intelligibility Assessment by Automated Segmental and Suprasegmental Listening Error Analysis. aug: au: Jiao, Yishan LaCross, Amy Berisha, Visar Liss, Julie affil: Department of Speech and Hearing Science, Arizona State University, Tempe School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe su: Linguistics Sensory perception Algorithms Dysarthria Listening Regression analysis Research evaluation Research funding Speech evaluation Speech perception Surveys Phonological awareness Data analysis software Descriptive statistics sug: subj: Linguistics Sensory perception Algorithms Dysarthria Listening Regression analysis Research evaluation Research funding Speech evaluation Speech perception Surveys Phonological awareness Data analysis software Descriptive statistics ab: Purpose: Subjective speech intelligibility assessment is often preferred over more objective approaches that rely on transcript scoring. This is, in part, because of the intensive manual labor associated with extracting objective metrics from transcribed speech. In this study, we propose an automated approach for scoring transcripts that provides a holistic and objective representation of intelligibility degradation stemming from both segmental and suprasegmental contributions, and that corresponds with human perception. Method: Phrases produced by 73 speakers with dysarthria were orthographically transcribed by 819 listeners via Mechanical Turk, resulting in 63,840 phrase transcriptions. A protocol was developed to filter the transcripts, which were then automatically analyzed using novel algorithms developed for measuring phoneme and lexical segmentation errors. The results were compared with manual labels on a randomly selected sample set of 40 transcribed phrases to assess validity. A linear regression analysis was conducted to examine how well the automated metrics predict a perceptual rating of severity and word accuracy. Results: On the sample set, the automated metrics achieved 0.90 correlation coefficients with manual labels on measuring phoneme errors, and 100% accuracy on identifying and coding lexical segmentation errors. Linear regression models found that the estimated metrics could predict a significant portion of the variance in perceptual severity and word accuracy. Conclusions: The results show the promising development of an objective speech intelligibility assessment that identifies intelligibility degradation on multiple levels of analysis. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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