<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

HMM-based phoneme speech recognition system for the control and command of industrial robots

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional...

HMM-based phoneme speech recognition system for the control and command of industrial robots

Published Online: Feb 05, 2021

Page range: -

Received: Jun 01, 2020

Accepted: Feb 05, 2021

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)

© 2021 Adwait Naik, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

HMM-based phoneme speech recognition system for the control and command of industrial robots

Published Online: Feb 05, 2021

Page range: -

Received: Jun 01, 2020

Accepted: Feb 05, 2021

DOI: https://doi.org/10.37705/TechTrans/e2021002

KeywordsSpeech recognition, phoneme, robotics, human-robot interaction (HRI), linear predictive coding (LPC), hidden Markov model (HMM)

© 2021 Adwait Naik, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Keywords
Speech recognition, phoneme, robotics, human-robot interaction (HRI), linear predictive coding (LPC), hidden Markov model (HMM)