Manual and Automatic Transcriptions in Dementia Detection from Speech

Jochen Weiner, Mathis Engelbart, Tanja Schultz


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.


 DOI: 10.21437/Interspeech.2017-112

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{Weiner2017,
  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},
  url={http://dx.doi.org/10.21437/Interspeech.2017-112}
}