Automation of Language Sample Analysis

Autor/inn/en	Liu, Houjun; MacWhinney, Brian; Fromm, Davida; Lanzi, Alyssa
Titel	Automation of Language Sample Analysis
Quelle	In: Journal of Speech, Language, and Hearing Research, 66 (2023) 7, S.2421-2433 (13 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (MacWhinney, Brian) ORCID (Fromm, Davida) ORCID (Lanzi, Alyssa)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Automation; Language Tests; Computational Linguistics; Morphology (Languages); Syntax; Auditory Perception; Speech Language Pathology; Language Impairments; Coding; Transcripts (Written Records); Accuracy + Suchen Sie Ihr Suchwort? Language test; Sprachtest; Linguistics; Computerlinguistik; Morphology; Morphologie; Auditive Wahrnehmung; Akustische Wahrnehmung; Akustik; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung; Codierung; Programmierung
Abstract	Purpose: A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and effort could help in promoting the use of LSA for clinical practice and research. Method: This article describes an automated pipeline, called Batchalign, that takes raw audio and creates full transcripts in Codes for the Human Analysis of Talk (CHAT) transcription format, complete with utterance-and word-level time alignments and morphosyntactic analysis. The pipeline only requires major human intervention for final checking. It combines a series of existing tools with additional novel reformatting processes. The steps in the pipeline are (a) automatic speech recognition, (b) utterance tokenization, (c) automatic corrections, (d) speaker ID assignment, (e) forced alignment, (f) user adjustments, and (g) automatic morphosyntactic and profiling analyses. Results: For work with recordings from adults with language disorders, six major results were obtained: (a) The word error rate was between 2.4% for controls and 3.4% for patients, (b) utterance tokenization accuracy was at the level reported for speakers without language disorders, (c) word-level diarization accuracy was at 93% for control participants and 83% for participants with language disorders, (d) utterance-level diarization accuracy based on word-level diarization was high, (e) adherence to CHAT format was fully accurate, and (f) human transcriber time was reduced by up to 75%. Conclusion: The pipeline dramatically shortens the time gap between data collection and data analysis and provides an output superior to that typically generated by human transcribers. (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	2024/1/01