A Speech-Based System for In-Home Emergency Detection and Remote Assistance

Principi E, Fuselli D, Squartini S, Bonifazi M, Piazza F. A Speech-Based System for In-Home Emergency Detection and Remote Assistance. In: AES Convention 134. Audio Engineering Society; 2013. Paper 8828. Available from: https://aes.org/publications/elibrary-page/?id=16729

@inproceedings{Principi2013_16729,
  author = {Principi, Emanuele and Fuselli, Danilo and Squartini, Stefano and Bonifazi, Maurizio and Piazza, Francesco},
  title = {{A Speech-Based System for In-Home Emergency Detection and Remote Assistance}},
  booktitle = {AES Convention 134},
  note = {Paper 8828},
  year = {2013},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16729}
}

TY  - CPAPER
TI  - A Speech-Based System for In-Home Emergency Detection and Remote Assistance
AU  - Principi, Emanuele
AU  - Fuselli, Danilo
AU  - Squartini, Stefano
AU  - Bonifazi, Maurizio
AU  - Piazza, Francesco
T2  - AES Convention 134
M1  - Paper 8828
PY  - 2013
DA  - 2013/05/06
UR  - https://aes.org/publications/elibrary-page/?id=16729
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes a system for the detection of emergency states and for the remote assistance of people in their own homes. Emergencies are detected recognizing distress calls by means of a speech recognition engine. When an emergency is detected, a phone call is automatically established with a relative or friend by means of a VoIP stack and an Acoustic Echo Canceller. Several low-consuming embedded units are distributed throughout the house to monitor the acoustic environment, and one central unit coordinates the system operation. This unit also integrates multimedia content delivery services and home automation functionalities. Being an ongoing project, this paper describes the entire system and then focuses on the algorithms implemented for the acoustic monitoring and the hands-free communication services. Preliminary experiments have been conducted to assess the performance of the recognition module in noisy and reverberated environments and the out of grammar rejection capabilities. Results showed that the implemented Power Normalized Cepstral Coefficients extraction pipeline improves the word recognition accuracy in noisy and reverberated conditions, and that introducing a "garbage phone" in the acoustic model allows to effectively reject out of grammar words and sentences.
ER  -