Towards automated dementia diagnosis based upon speech

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 effects particular brain regions involved in speech production and processing will also leave detectable finger prints in the speech. The goal of this work is an easy-to-use, non-invasive, inexpensive diagnostic test for dementia that can easily be applied in a clinician’s office or even at home. Experimental evidence suggests 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 a transcription of the speech, and results of a mini mental state exam. Progress is reported toward a fully automatic speech recognition system tuned for the speech-to-text aspect of this application. In addition to using state-of-the- art automatic speech recognition techniques such as Deep Learning, recurrent neural networks are used to predict the punctuation in transcribed speech, which is later used for extracting linguistic features. This fully automated system for 73 speakers is combined with 140 manually transcribed speech samples and used for experimental testing of a system for automated detection of Alzheimer’s from speech.