HMM-based phoneme speech recognition system for the control and command of industrial robots
Author
Naik, Adwait
Published in
Technical Transactions
Numbering
Vol. 118, iss. 1
Release date
2021
Place of publication
Warsaw
Publisher
Sciendo
Magazine section
Mechanics
Language
English
eISSN
2353-737X
DOI
https://doi.org/10.37705/TechTrans/e2021002
Keywords
speech recognition, phoneme, robotics, human-robot interaction (HRI), linear predictive coding (LPC), hidden Markov model (HMM)
Abstract
In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (robotic-arm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.
