  Dementia is a syndrome, generally of a chronic nature characterized by a
deterioration in cognitive function, especially in the geriatric population and
is severe enough to impact their daily activities. Early diagnosis of dementia
is essential to provide timely treatment to alleviate the effects and sometimes
to slow the progression of dementia. Speech has been known to provide an
indication of a person's cognitive state. The objective of this work is to use
speech processing and machine learning techniques to automatically identify the
stage of dementia such as mild cognitive impairment (MCI) or Alzheimers disease
(AD). Non-linguistic acoustic parameters are used for this purpose, making this
a language independent approach. We analyze the patients audio excerpts from a
clinician-participant conversations taken from the Pitt corpus of DementiaBank
database, to identify the speech parameters that best distinguish between MCI,
AD and healthy (HC) speech. We analyze the contribution of various types of
acoustic features such as spectral, temporal, cepstral their feature-level
fusion and selection towards the identification of dementia stage.
Additionally, we compare the performance of using feature-level fusion and
score-level fusion. An accuracy of 82% is achieved using score-level fusion
with an absolute improvement of 5% over feature-level fusion.
