ArticleIEEE journal of selected topics in signal processing2025
Automatic Detection of Articulatory-Based Disfluencies in Primary Progressive Aphasia.
Article in IEEE journal of selected topics in signal processing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence-based speech error detection to differentiate primary progressive aphasia variants.Brain communications · 2026Article
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11 authors.
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Abstract
Speech corpora are collections of textual data derived from human verbal output and speech signals that can be processed from a variety of perspectives, including formal or semantic content, to serve analyses of different levels of linguistic organisation (phonemic, morphosyntactic, lexico-semantic and content information, prosody and intonation) and to serve analyses of important phenomena such as speech fluency and errors (non-fluencies). We focus on transcribing speech along with non-fluencies or dysfluencies, the detection of which plays an important role in the diagnosis of primary progressive aphasia, where we specifically examine articulation-based dysfluencies in nfvPPA speech. In this work, we propose SSDM 2.0, which is built on top of the current state-of-the-art system of dysfluency detection [1] and tackles its shortcomings via four main contributions: (1) We propose a novel
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