ISCA Archive Interspeech 2008
ISCA Archive Interspeech 2008

Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment

Michel Vacher, Anthony Fleury, Jean-Fran├žois Serignat, Norbert Noury, Hubert Glasson

Improvements in medicine increase life expectancy and the number of elderly persons, but the institutions able to welcome them are not sufficient. A lot of projects work on ways allowing elderly persons to stay at home. This article describes the implementation of a sound classification and speech recognition system equipping a real flat. This system has been evaluated in uncontrolled conditions for distinguishing normal sentences from distress ones; these sentences are uttered by heterogeneous speakers. The detected signals are first classified as sound and speech. The sounds are clustered in eight classes (object fall, doors clap, phone ringing, steps, dishes, doors lock, screams and glass breaking). As for speech signals, an input utterance (in French) is recognized and a subsequent process classifies it in normal or distress, by analysing the presence of distress key words. In the same way, some sound classes are related to a possible distress situation. An experimental protocol was defined and tested in real conditions inside the flat. Finally, we discuss the results of this experiment, where ten subjects were involved.


doi: 10.21437/Interspeech.2008-84

Cite as: Vacher, M., Fleury, A., Serignat, J.-F., Noury, N., Glasson, H. (2008) Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment. Proc. Interspeech 2008, 496-499, doi: 10.21437/Interspeech.2008-84

@inproceedings{vacher08_interspeech,
  author={Michel Vacher and Anthony Fleury and Jean-Fran├žois Serignat and Norbert Noury and Hubert Glasson},
  title={{Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment}},
  year=2008,
  booktitle={Proc. Interspeech 2008},
  pages={496--499},
  doi={10.21437/Interspeech.2008-84}
}