Design of voice communication-based teaching assistant robot
Corressponding author's email:
sontn@hcmute.edu.vnKeywords:
Assistant robots, Natural language processing, Long Short-Term Memory Network, Machine learning, Speech processingAbstract
This study presents a design and testing of a teaching assistant robot that works as a virtual assistant being capable voice communication with human (working as a virtual assistant that is capable of communicating using voice with humans). Robot(s) can work without connecting to the network, the open source Pocketsphinx is employed for speech recognition. The Pocketsphinx module is followed by a correction module to improve the accuracy. The Long Short-Term Memory is utilized for (the) natural language processing unit that produces the answers. The model is deployed on the low-cost embedded board, Raspberry Pi Zero. The evaluation was performed with and without using the proposed correction module. The accuracy is 62.5% when using Pocketsphinx without the proposed correction module. With the proposed correction module, the robot improved the identifying and answering questions capacities to 87.2% of accuracy.
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