ArticleJournal of speech, language, and hearing research : JSLHR2024
Automatic Speech Recognition in Primary Progressive Apraxia of Speech.
Article in Journal of speech, language, and hearing research : JSLHR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning-derived measures of sound-level accuracy in primary progressive apraxia of speech: A feasibility pipeline with descriptive evidence from two cases.Clinical linguistics & phonetics · 2026Article
- Artificial intelligence-based speech error detection to differentiate primary progressive aphasia variants.Brain communications · 2026Article
- Automated Assessment of Word- and Sentence-Level Speech Intelligibility in Developmental Motor Speech Disorders: A Cross-Linguistic Investigation.Diagnostics (Basel, Switzerland) · 2025Article
- Automated differentiation of non-fluent and logopenic primary progressive aphasia in Italian speakers using acoustic and linguistic speech measures.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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8 authors.
Funding
Abstract
introductionTranscribing disordered speech can be useful when diagnosing motor speech disorders such as primary progressive apraxia of speech (PPAOS), who have sound additions, deletions, and substitutions, or distortions and/or slow, segmented speech. Since transcribing speech can be a laborious process and requires an experienced listener, using automatic speech recognition (ASR) systems for diagnosis and treatment monitoring is appealing. This study evaluated the efficacy of a readily available ASR system (wav2vec 2.0) in transcribing speech of PPAOS patients to determine if the word error rate (WER) output by the ASR can differentiate between healthy speech and PPAOS and/or among its subtypes, whether WER correlates with AOS severity, and how the ASR's errors compare to those noted in manual transcriptions.
methodForty-five patients with PPAOS and 22 healthy controls were recorded repeating 13 words, 3 times each, which were transcribed manually and using wav2vec 2.0. The WER and phonetic and prosodic speech errors were compared between groups, and ASR results were compared against manual transcriptions.
resultsMean overall WER was 0.88 for patients and 0.33 for controls. WER significantly correlated with AOS severity and accurately distinguished between patients and controls but not between AOS subtypes. The phonetic and prosodic errors from the ASR transcriptions were also unable to distinguish between subtypes, whereas errors calculated from human transcriptions were. There was poor agreement in the number of phonetic and prosodic errors between the ASR and human transcriptions.
conclusionsThis study demonstrates that ASR can be useful in differentiating healthy from disordered speech and evaluating PPAOS severity but does not distinguish PPAOS subtypes. ASR transcriptions showed weak agreement with human transcriptions; thus, ASR may be a useful tool for the transcription of speech in PPAOS, but the research questions posed must be carefully considered within the context of its limitations. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.26359417.
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