Manual and Automatic Transcription in Dementia Detection from Speechby Jochen Weiner, Mathis Engelbart, Tanja Schultz
Abstract:
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.
Reference:
Manual and Automatic Transcription in Dementia Detection from Speech (Jochen Weiner, Mathis Engelbart, Tanja Schultz), In INTERSPEECH 2017 – 18\textsuperscriptth Annual Conference of the International Speech Communication Association, 2017.
Bibtex Entry:
@inproceedings{weiner2017manual,
title={{Manual and Automatic Transcription in Dementia Detection from Speech}},
author={Jochen Weiner and Mathis Engelbart and Tanja Schultz},
booktitle={{INTERSPEECH} 2017 -- 18\textsuperscript{th} Annual Conference of the International Speech Communication Association},
year={2017},
abstract={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.},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/Interspeech2017_WeinerEtAl.pdf},
poster={http://www.csl.uni-bremen.de/cms/images/documents/publications/Interspeech2017_WeinerEtAl_poster.pdf},
}