Open AccessEnvironmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction
Author(s) -
Benjamin Birch,
Christian Griffiths,
Adam Morgan
Publication year - 2021
Publication title -
proceedings of the institution of mechanical engineers. part b, journal of engineering manufacture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.861
H-Index - 64
eISSN - 2041-2975
pISSN - 0954-4054
DOI - 10.1177/09544054211014492
Subject(s) - dynamic time warping , computer science , robot , mel frequency cepstrum , speech recognition , human–robot interaction , interface (matter) , utterance , reliability (semiconductor) , feature (linguistics) , feature extraction , human–computer interaction , artificial intelligence , power (physics) , linguistics , physics , philosophy , bubble , quantum mechanics , maximum bubble pressure method , parallel computing
Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.