Emergency Voice/Stress-Level Combined Recognition for Intelligent House Applications

Drosos K, Floros A, Agavanakis K, Tatlas NA, Kanellopoulos NG. Emergency Voice/Stress-Level Combined Recognition for Intelligent House Applications. In: AES Convention 132. Audio Engineering Society; 2012. Paper 8615. Available from: https://aes.org/publications/elibrary-page/?id=16253

@inproceedings{Drosos2012_16253,
  author = {Drosos, Konstantinos and Floros, Andreas and Agavanakis, Kyriakos and Tatlas, Nicolas-Alexander and Kanellopoulos, Nikolaos-Grigorios},
  title = {{Emergency Voice/Stress-Level Combined Recognition for Intelligent House Applications}},
  booktitle = {AES Convention 132},
  note = {Paper 8615},
  year = {2012},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16253}
}

TY  - CPAPER
TI  - Emergency Voice/Stress-Level Combined Recognition for Intelligent House Applications
AU  - Drosos, Konstantinos
AU  - Floros, Andreas
AU  - Agavanakis, Kyriakos
AU  - Tatlas, Nicolas-Alexander
AU  - Kanellopoulos, Nikolaos-Grigorios
T2  - AES Convention 132
M1  - Paper 8615
PY  - 2012
DA  - 2012/04/06
UR  - https://aes.org/publications/elibrary-page/?id=16253
PB  - Audio Engineering Society
LA  - en
AB  - Legacy technologies for word recognition can benefit from emerging affective voice retrieval, potentially leading to intelligent applications for smart houses enhanced with new features. In this work we introduce the implementation of a system, capable to react to common spoken words, taking into account the estimated vocal stress level, thus allowing the realization of a prioritized, affective aural interaction path. Upon the succesful word recognition and the corresponding stress level estimation, the system triggers particular affective-prioritized actions, defined within the application scope of an intelligent home environment. Application results show that the established affective interaction path significantly improves the ambient intelligence provided by an affective vocal sensor that can be easily integrated with any sensor-based home monitoring system.
ER  -