Document Type
Conference Proceeding
Publication Date
8-2017
Abstract
The clinical diagnosis of Alzheimer’s disease and other dementias is very challenging, especially in the early stages. Our hypothesis is that any disease that affects particular brain regions involved in speech production and processing will also leave detectable finger prints in the speech. Computerized analysis of speech signals and computational linguistics have progressed to the point where an automatic speech analysis system is a promising approach for a low-cost non-invasive diagnostic tool for early detection of Alzheimer’s disease.
We present empirical evidence that strong discrimination between subjects with a diagnosis of probable Alzheimer’s versus matched normal controls can be achieved with a combination of acoustic features from speech, linguistic features extracted from an automatically determined transcription of the speech including punctuation, and results of a mini mental state exam (MMSE). We also show that discrimination is nearly as strong even if the MMSE is not used, which implies that a fully automated system is feasible. Since commercial automatic speech recognition (ASR) tools were unable to provide transcripts for about half of our speech samples, a customized ASR system was developed.
Recommended Citation
Sadeghian, R., Schaffer, J. D., & Zahorian, S. A.
(2017). Speech Processing Approach for Diagnosing Dementia in an Early Stage. Interspeech 2017, 2705-2709.
Publication Title
Interspeech 2017
Start Page No.
2705
End Page No.
2709
DOI
10.21437/Interspeech.2017-1712
Included in
Analysis Commons, Diseases Commons, Neurology Commons, Speech Pathology and Audiology Commons