Model adaptation and adaptive training for the recognition of dysarthric speech
Open accessAbstract
Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech production system of an individual and can have a detrimental effect on the speech output.In addition to the data sparseness problems, dysarthric speech is characterised by inconsistencies in the acoustic space making it extremely challenging to model.This paper investigates a variety of baseline speaker independent (SI) systems and its suitability for adaptation.The study also explores the usefulness of speaker adaptive training (SAT) for implicitly annihilating inter-speaker variations in a dysarthric corpus.The paper implements a hybrid MLLR-MAP based approach to adapt the SI and SAT systems.ALL the results reported uses UA-SPEECH dysarthric data.Our best adapted systems gave a significant absolute gain of 11.05% (20.42% relative) over the last published best result in the literature.A statistical analysis performed across various systems and its specific implementation in modelling different dysarthric severity sub-groups, showed that, SAT-adapted systems were more applicable to handle disfluencies of more severe speech and SI systems prepared from typical speech were more apt for modelling speech with low level of severity.
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Exploiting Cross Domain Acoustic-to-Articulatory Inverted Features for Disordered Speech Recognition
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- Authors
- Sehgal, S., & Cunningham, S.
- Affiliations
- University of Sheffield (GB)
- ORCID
- Cunningham, Stuart
- Venue
- Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies
- Published
- 2015
- Pages
- 65-71
- ISSN
- 2411-9962 (ISSN-L)
- OpenAlex
- W2250686550
- Cited by
- 47
- References
- 25
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 9 · 2023: 9 · 2022: 2 · 2021: 8 · 2020: 4 · 2019: 2 · 2018: 1 · 2017: 2 · 2016: 2
- FWCI
- 0.94
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Speech and Language Processing for Assistive Technologies (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- National Institute for Health and Care Research (II-LB-0214-20008)
- Invention for Innovation Programme
- Invention for Innovation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- dysarthria, dysarthric speech recognition, speaker adaptive training, model adaptation, maximum likelihood linear regression, maps, speaker variability, dysarthria severity, disfluency, data sparsity
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