Bengali Speech Recognition: An Overview
Mashuk Arefin Pranjol, Farhin Rahman, Ehsanur Rahman Rhythm, Rajvir Ahmed Shuvo, Tanjib Ahmed, Bushra Yesmeen Anika, Md. Abdullah Al Masum Anas, Jahidul Hasan, Saiadul Arfain, Shadab Iqbal, Md Humaion Kabir Mehedi, Annajiat Alim Rasel · 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · 2022
This study outlines the notable efforts of creating of automatic speech recognition (ASR) system in Bengali. It describes data from the Bengali language's existing voice corpus and the major reports that have contributed to the recent research scenario. It provides an overview of dataset or corpus that has been created for bengali ASR, challenge faced to create bengali ASR as well as techniques used to build Bengali ASR system. ASR techniques for the Bengali language have made significant progress in recent years. Our article contains studies from 2016 through 2020. We examined the results of these investigations, as well as the strategies used to accomplish this goal, for Automated voice recognition. We have examined these publications to obtain a feel of the present state of Bengali ASR. We have observed a dearth of sufficient datasets among these researchers, which is important for any automated system. Due to the language's abundance of consonant clusters, the Machine Learning (ML) system has difficulty interpreting Bengali words. As a result of these modifications, the system now confronts a new set of difficulties in terms of effectiveness and efficiency. Additionally, numerous words have nearly identical pronunciations. These are only some of the issues that the papers we examined face. This research makes use of a variety of techniques, including linear prediction coding, Mel Frequency Cepstral Coefficient, Hidden Markov Model, Neural Network, and Fuzzy logic. Bengali ASR will require further investigation shortly. While recent research is encouraging, ASR of other languages, such as English, is far from perfect and efficient.