Mild Cognitive Impairment (MCI), sometimes regarded as a prodromal stage of Alzheimer's disease, is a mental disorder that is difficult to diagnose. However, recent studies reported that MCI causes slight changes in the speech of the patient. Our starting point here is a study that found acoustic correlates of MCI, but extracted the proposed features manually. Here, we automate the extraction of the features by applying automatic speech recognition (ASR). Unlike earlier authors, we use ASR to extract only a phonetic level segmentation and annotation. While the phonetic output allows the calculation of features like the speech rate, it avoids the problems caused by the agrammatical speech frequently produced by the targeted patient group. Furthermore, as hesitation is the most important indicator of MCI, we take special care when handling filled pauses, which usually correspond to hesitation. Using the ASR-based features, we employ machine learning methods to separate the subjects with MCI from the control group. The classification results obtained with ASR-based feature extraction are just slightly worse that those got with the manual method. The F1 value achieved (85.3) is very promising regarding the creation of an automated MCI screening application.
Cite as: Tóth, L., Gosztolya, G., Vincze, V., Hoffmann, I., Szatlóczki, G., Biró, E., Zsura, F., Pákáski, M., Kálmán, J. (2015) Automatic detection of mild cognitive impairment from spontaneous speech using ASR. Proc. Interspeech 2015, 2694-2698, doi: 10.21437/Interspeech.2015-568
@inproceedings{toth15_interspeech,
author={László Tóth and Gábor Gosztolya and Veronika Vincze and Ildikó Hoffmann and Gréta Szatlóczki and Edit Biró and Fruzsina Zsura and Magdolna Pákáski and János Kálmán},
title={{Automatic detection of mild cognitive impairment from spontaneous speech using ASR}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={2694--2698},
doi={10.21437/Interspeech.2015-568}
}