An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer’s Dementia in Spontaneous Speech
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 163482209
- Type
- D - Journal article
- DOI
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10.1109/JSTSP.2019.2955022
- Title of journal
- IEEE Journal of Selected Topics in Signal Processing
- Article number
- -
- First page
- 272
- Volume
- 14
- Issue
- 2
- ISSN
- 1932-4553
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
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B - Data Science and Artificial Intelligence
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduced a novel unsupervised acoustic feature extraction technique for use in Alzheimer's dementia detection from spontaneous speech samples. This approach was shown to achieve detection performance equal or better than the performance achieved by approaches that rely on speech transcripts. These results formed the basis for an MRC Transition Fellowship awarded to one of the researchers in my group and co-author (S. de la Fuente) to pursue research on translating this method to clinical practice.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -