Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease
(AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive
decline in mild cognitive impairment (MCI). Since the presence of language impairment has been
reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological
screening method which is based on the analysis of spontaneous speech production during performing a
memory task. In the future, this can form the basis of an Internet-based interactive screening software
for the recognition of MCI.
Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked
spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct,
one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo,
length and number of silent and filled pauses, length of utterance) were extracted from the recorded
speech signals, first manually (using the Praat software), and then automatically, with an automatic
speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then
we applied machine learning algorithms to see whether the MCI and the control group can be discriminated
automatically based on the acoustic features.
Results: The statistical analysis showed significant differences for most of the acoustic parameters
(speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance
ratio). The most significant differences between the two groups were found in the speech tempo in the
delayed recall task, and in the number of pauses for the question-answering task. The fully automated
version of the analysis process – that is, using the ASR-based features in combination with machine
learning - was able to separate the two classes with an F1-score of 78.8%.
Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new,
automatic detection-based tool for screening MCI for the community.