Automation of Language Sample Analysis.


Journal

Journal of speech, language, and hearing research : JSLHR
ISSN: 1558-9102
Titre abrégé: J Speech Lang Hear Res
Pays: United States
ID NLM: 9705610

Informations de publication

Date de publication:
12 07 2023
Historique:
pmc-release: 01 01 2024
medline: 14 7 2023
pubmed: 23 6 2023
entrez: 22 6 2023
Statut: ppublish

Résumé

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. 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. 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%. 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.

Identifiants

pubmed: 37348510
doi: 10.1044/2023_JSLHR-22-00642
pmc: PMC10555460
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

2421-2433

Subventions

Organisme : NIDCD NIH HHS
ID : R01 DC008524
Pays : United States

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Auteurs

Houjun Liu (H)

The Nueva School, San Mateo, CA.

Brian MacWhinney (B)

Department of Psychology, Carnegie Mellon University, Pittsburgh, PA.

Davida Fromm (D)

Department of Psychology, Carnegie Mellon University, Pittsburgh, PA.

Alyssa Lanzi (A)

Communication Sciences and Disorders Department, University of Delaware, Newark.

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