Speech Processing Approach for Diagnosing Dementia in an Early Stage
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Intra-subject analysis showed good specificity in classifying bipolar subjects and healthy controls across all F0 contour features, but the directions of most features were inconsistent across subjects.
“They noted that intra-subject analysis showed good specificity in classifying bipolar subjects and healthy controls across all contour features, but that directions of most were not consistent across different subjects.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [41]
An automated method estimating the F0 contour over time with a moving window can serve as a proxy for mood changes in bipolar disorder.
“In this study, the authors propose an automated method for estimating the contour of F0 over time with a moving window approach as a proxy for mood changes.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [41]
In certain studies, MMSE is used to calculate the baseline against which classifier performance will be measured.
“In certain studies, MMSE is used to calculate the baseline against which classifier performance will be measured [44, 70, 71].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [44]
Very few reviewed papers report use of artificial neural networks, recurrent neural networks, multi-layer perceptron, or convolutional neural networks.
“As regards choice of machine learning methods, very few papers report the use of artificial neural networks [91, 95], recurrent neural networks [82], multi-layer perceptron [44, 67, 79, 80, 86], or convolutional neural networks [60, 85].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [44]
Off-the-shelf ASR tools are not readily prepared for dementia research, and therefore some reviewed studies developed their own custom ASR systems.
“It is becoming increasingly obvious that off-the-shelf ASR tools are not readily prepared for dementia research, and therefore some reviewed studies developed their own custom ASR systems [44, 63].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [44]
Cognitive scores, particularly MMSE, are available with many datasets, including the Pitt Corpus, but mostly remain unused except for diagnostic group assignments or rarely as baseline performance.
“Cognitive scores, particularly MMSE, are available with many datasets, including the most commonly used, Pitt Corpus. However, these scores mostly remain unused except for diagnostic group assignments, or more rarely, as baseline performance [44, 70, 71]”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [44]
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References 12 · Cited by 40
- Authors
- Sadeghian, R., Schaffer, J. D., & Zahorian, S. A.
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 2705-2709
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2748716413
- Cited by
- 40
- References
- 12
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 12 · 2022: 2 · 2021: 6 · 2020: 8 · 2019: 5 · 2018: 2
- FWCI
- 11.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- dementia diagnosis, early detection, speech processing, speech analysis, cognitive impairment
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