As the population in developed countries is aging, larger numbers of
people are at risk of developing dementia. In the near future there
will be a need for time- and cost-efficient screening methods. Speech
can be recorded and analyzed in this manner, and as speech and language
are affected early on in the course of dementia, automatic speech processing
can provide valuable support for such screening methods.
We present two pipelines
of feature extraction for dementia detection: the manual pipeline
uses manual transcriptions while the fully automatic pipeline uses
transcriptions created by automatic speech recognition (ASR). The acoustic
and linguistic features that we extract need no language specific tools
other than the ASR system. Using these two different feature extraction
pipelines we automatically detect dementia. Our results show that the
ASR system’s transcription quality is a good single feature and
that the features extracted from automatic transcriptions perform similar
or slightly better than the features extracted from the manual transcriptions.
Cite as: Weiner, J., Engelbart, M., Schultz, T. (2017) Manual and Automatic Transcriptions in Dementia Detection from Speech. Proc. Interspeech 2017, 3117-3121, doi: 10.21437/Interspeech.2017-112
@inproceedings{weiner17_interspeech,
author={Jochen Weiner and Mathis Engelbart and Tanja Schultz},
title={{Manual and Automatic Transcriptions in Dementia Detection from Speech}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={3117--3121},
doi={10.21437/Interspeech.2017-112}
}