Speech to text translation enabling multilingualism.

Bano, Shahana; Jithendra, Pavuluri; Niharika, Gorsa Lakshmi; Sikhi, Yalavarthi

Abstract

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language.

Citation

BANO, S., JITHENDRA, P., NIHARIKA, G.L. and SIKHI, Y. 2020. Speech to text translation enabling multilingualism. In Proceedings of the 2020 International conference for innovation in technology (INOCON 2020), 6-8 November 2020, Bangluru, India. Piscataway: IEEE [online]. Available from: https://doi.org/10.1109/INOCON50539.2020.9298280

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 International conference for innovation in technology (INOCON 2020)
• Start Date: Nov 6, 2020
• End Date: Nov 8, 2020
• Acceptance Date: Jul 30, 2020
• Online Publication Date: Jan 1, 2021
• Publication Date: Dec 31, 2020
• Deposit Date: Sep 20, 2023
• Publicly Available Date: Sep 20, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• ISBN: 9781728197456
• DOI: https://doi.org/10.1109/INOCON50539.2020.9298280
• Keywords: Speech recognition; Natural language processing; Language translation; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2064062