Speech-to-Text Conventional Myanmar (Burmese) Language Recognition System

Halawati Binti Abd Jalil; Pyae Sone Phyo; Md Amin Ullah Sheikh; Riskhan Basheer

doi:10.20944/preprints202406.1696.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 23 June 2024 / Approved: 24 June 2024 / Online: 24 June 2024 (13:49:30 CEST)

Only languages with abundant resources are given the opportunity to build speech recognition systems, whereas low resource languages are disregarded. This project attempts to create a system that can normally convert speech to text in the Myanmar (Burmese) language, a language part of low resource languages. The Burmese language is extremely intricate and is made up of vowels, consonants, tones, and stress. There are 10 numbers, 12 vowels, and 33 letters in the Myanmar alphabet. Deep learning allows for the evolution of numerous algorithms while eradicating ignorance and even low-resource languages may be turned into speech-to-text systems with a small quantity of data. The procedures required to construct a successful speech recognition system that can assist both the corporate sector and society will be covered in this project, including data collecting, preprocessing, modeling, prototyping, assessment, and ultimately deployment.

Speech to Text; NLP; Language Recognition

Computer Science and Mathematics, Computer Science

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Speech-to-Text Conventional Myanmar (Burmese) Language Recognition System

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