Document Type

Conference Proceeding

Publication Date



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.

Publication Title

Interspeech 2017

Start Page No.


End Page No.






To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.