Version 1
: Received: 6 April 2024 / Approved: 8 April 2024 / Online: 9 April 2024 (09:40:31 CEST)
How to cite:
Majumdar, S.; Das, K.; Chowdhury, O.A.; Yadav, A.P.; Sahoo, B. A method for automatically detecting errors in an embedded English speech teaching system. Preprints2024, 2024040611. https://doi.org/10.20944/preprints202404.0611.v1
Majumdar, S.; Das, K.; Chowdhury, O.A.; Yadav, A.P.; Sahoo, B. A method for automatically detecting errors in an embedded English speech teaching system. Preprints 2024, 2024040611. https://doi.org/10.20944/preprints202404.0611.v1
Majumdar, S.; Das, K.; Chowdhury, O.A.; Yadav, A.P.; Sahoo, B. A method for automatically detecting errors in an embedded English speech teaching system. Preprints2024, 2024040611. https://doi.org/10.20944/preprints202404.0611.v1
APA Style
Majumdar, S., Das, K., Chowdhury, O.A., Yadav, A.P., & Sahoo, B. (2024). A method for automatically detecting errors in an embedded English speech teaching system. Preprints. https://doi.org/10.20944/preprints202404.0611.v1
Chicago/Turabian Style
Majumdar, S., Ashutosh Prasad Yadav and Bapun Sahoo. 2024 "A method for automatically detecting errors in an embedded English speech teaching system" Preprints. https://doi.org/10.20944/preprints202404.0611.v1
Abstract
This enables the chip to regulate the voice recognition function of the gadget, leading to a more natural and human-like process. Integrating this technique into the English language voice recognition system can greatly improve its precision in identifying the speech of a particular person. This research seeks to investigate and create an automatic error detection technique for an embedded speech instruction recognition system for English using artificial intelligence. The study commences with a conventional introduction to artificial intelligence, thereafter delving into a comprehensive investigation of the speech recognition algorithm. The MATLAB programme is used to determine the accurate number of words recognised by the system and its accuracy rate. The embedded teaching recognition system for English is then tested in different contexts to assess its performance. Several tests are performed to compare and analyse the results, which indicate that the integrated English voice recognition system achieves an accuracy rate of over 90% and has a low error rate in a calm environment. In a bustling environment with numerous auditory stimuli, the accurate recognition rate typically exceeds 60%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
