Speech activation for Internet of things security system in public utility vehicles and Taxicabs

• Main Authors: Palconit, Maria Gemel B., Formentera, April L., Aying, Renato J., Dianon, Kenny Jay A., Tadle, Joel B., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Transportation—Security measures > Automation; Automatic speech recognition; Internet of things; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2094; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3093/type/native/viewcontent
• Institution: De La Salle University

Public transport vehicles are widely preferred by the mass because of the accessibility it provides. Due to its easy access, crimes like robbery, assaults and even homicides are experienced by the drivers. Hence, vehicle tracking, and alert systems are built to improve its security and safety. The existing systems are limited to physical triggering which offers minimal effectiveness because the buttons may unintentionally be pressed, or the driver is hesitant to move and unable to press the button when necessary. To eliminate the inconvenience caused by a physically triggered security system, a non-contact activation was developed with the use of speech recognition, and the Internet of Things (IoT). This study presents the evaluation of the transcription confidence level associated with background noises using the Google Speech Recognition API and the implementation of the security system in IoT. The results show that speech recognition has acquired 100% transcription accuracy around 50 dBA to 78 dBA background noise using the native language, while the tested operation latency is approximately 43 seconds during the deployment. The study paved a way for a convenient noncontact triggering security system to elevate the rapid response of crime-related incidents in public vehicle drivers through immediate notification and provision of the necessary information to authorities. © 2019 IEEE.