ArticleJournal of speech, language, and hearing research : JSLHR2023
Automation of Language Sample Analysis.
Article in Journal of speech, language, and hearing research : JSLHR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
- Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech.Behavior research methods · 2026Article
- Theoretical and Methodological Insights From Five Transcribed and Annotated Daylong Audio Recordings of Young Children's Language Environments.Perspectives on psychological science : a journal of the Association for Psychological Science · 2026Article
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Automatically Calculated Context-Sensitive Features of Connected Speech Improve Prediction of Impairment in Alzheimer's Disease.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Introducing the NEURAL Research Lab Data Set for Studies of Discourse and Gesture in Aphasia and Cognitively Healthy Aging Adults.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Autoscribe: An automated tool for creating transcribed TextGrids from audio-recorded conversations.Behavior research methods · 2025Article
- Priorities for New Data Collection.Developmental science · 2025Article
- Understanding Language through TalkBank.Current directions in psychological science · 2025Article
- Investigating changes in connected speech in nonfluent/agrammatic primary progressive aphasia following script training.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- It's About Time: Parent-Child Turn-Taking in Early Stuttering.American journal of speech-language pathology · 2025Observational
- The curious case of the Cat Rescue: can picture narrative description inform visuospatial processing in aphasia?Frontiers in human neuroscience · 2025Article
- Reliability of the Brief Assessment of Transactional Success in Communication in Aphasia.Aphasiology · 2025Article
- Role of pause duration in primary progressive aphasia.Aphasiology · 2025Article
- Automated Analysis of Fluency Behaviors in Aphasia.Journal of speech, language, and hearing research : JSLHR · 2024Article
- Clinical efficacy of pre-trained large language models through the lens of aphasia.Scientific reports · 2024Article
- Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features.American journal of speech-language pathology · 2024Article
- Story Grammar Analyses Capture Discourse Improvement in the First 2 Years Following a Severe Traumatic Brain Injury.American journal of speech-language pathology · 2024Article
- Wishes, beliefs, and jealousy: use of mental state terms inFrontiers in human neuroscience · 2024Article
- The Case of the Cookie Jar: Differences in Typical Language Use in Dementia.Journal of Alzheimer's disease : JAD · 2024Article
- Redefining the norms: an interdisciplinary perspective on language testing in multilinguals with acquired and progressive neurogenic disorders.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeA 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.
methodThis 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.
resultsFor 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%.
conclusionThe pipeline dramatically shortens the time gap between data collection and data analysis and provides an output superior to that typically generated by human transcribers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.