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.
