Smartphone-Assisted Pronunciation Learning Technique for Ambient Intelligence
In an ambient intelligence (AmI) environment, electronic devices that comprise the Internet of things (IoT) network work together seamlessly to provide a wide variety of applications and intelligent services to users. Computer-assisted pronunciation training (CAPT), a widely used application in the traditional Internet environment that corrects userÔ??s pronunciation, is a promising service for transition to the AmI environment. However, the migration of the CAPT to the AmI environment is challenging due to its high computational requirements that is at odds with the low computational capacity of IoT members. In this paper, we propose a smartphone-assisted pronunciation learning technique based on a lightweight word recommendation method that exploits built-in functions supported by IoT members and a computationally moderate word selection method. The experimental evaluation of the proposed method demonstrates that the user pronunciation is significantly improved without incurring unacceptable computational costs for a smartphone platform.
Ambient intelligence, Internet of things, intelligent activity, computer-assisted pronunciation training system, educational data mining, bag of phonemes, word recommendation.