An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription

Autor/inn/en	Fox, Carly B.; Israelsen-Augenstein, Megan; Jones, Sharad; Gillam, Sandra Laing
Titel	An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription
Quelle	In: Journal of Speech, Language, and Hearing Research, 64 (2021) 9, S.3533-3548 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Fox, Carly B.) ORCID (Israelsen-Augenstein, Megan) ORCID (Jones, Sharad) ORCID (Gillam, Sandra Laing)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Transcripts (Written Records); Child Language; Narration; Language Impairments; Developmental Disabilities; Automation; Speech Language Pathology; Allied Health Personnel; Accuracy; Reliability + Suchen Sie Ihr Suchwort? 'Children''s language'; Kindersprache; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung; Entwicklungsstörung; Reliabilität
Abstract	Purpose: This study examined the accuracy and potential clinical utility of two expedited transcription methods for narrative language samples elicited from school-age children (7;5-11;10 [years;months]) with developmental language disorder. Transcription methods included real-time transcription produced by speech-language pathologists (SLPs) and trained transcribers (TTs) as well as Google Cloud Speech automatic speech recognition. Method: The accuracy of each transcription method was evaluated against a gold-standard reference corpus. Clinical utility was examined by determining the reliability of scores calculated from the transcripts produced by each method on several language sample analysis (LSA) measures. Participants included seven certified SLPs and seven TTs. Each participant was asked to produce a set of six transcripts in real time, out of a total 42 language samples. The same 42 samples were transcribed using Google Cloud Speech. Transcription accuracy was evaluated through word error rate. Reliability of LSA scores was determined using correlation analysis. Results: Results indicated that Google Cloud Speech was significantly more accurate than real-time transcription in transcribing narrative samples and was not impacted by speech rate of the narrator. In contrast, SLP and TT transcription accuracy decreased as a function of increasing speech rate. LSA metrics generated from Google Cloud Speech transcripts were also more reliably calculated. Conclusions: Automatic speech recognition showed greater accuracy and clinical utility as an expedited transcription method than real-time transcription. Though there is room for improvement in the accuracy of speech recognition for the purpose of clinical transcription, it produced highly reliable scores on several commonly used LSA metrics. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01