A Deep Learning Approach for Bangla Speech to Text Conversion

Adhikary, Raima; Fatema, Kaniz; Sifat-E-Jahan,; Yousuf, Mohammad Abu

doi:10.1109/icievicivpr52578.2021.9564239

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Fang Zheng and Guoliang Zhang [7] in the year 2020, they performed experiments on the MFCC and concluded that FEB can be used along with MFCC to minimize the syllable error rate which helps a lot for our proposed implementation. Raima Adhikary and Kaniz Fatema [8] in the year 2021, The proposed work focuses especially on the recognition of the bangala words using gated recurrent units (GRU) integrated with MFCC, where test data is small data set with test accuracy rate 47% only. Ahmed J Abougarair and Mohamed KI Aburakhis [9] in the year 2022, the main approach of paper is voice assistance with the assistance name alice that can be integrated with any IoT device, chat bots and many other.…”

Section: Literature Surveymentioning

confidence: 99%

Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing

Katuri¹,

Salugu²,

Tharuni³

et al. 2022

IJEAT

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One of the most crucial aspects of communication in daily life is speech recognition. Speech recognition that is based on natural language processing is one of the essential elements in the conversion of one system to another. In this paper, we created an interface that transforms speech and other auditory inputs into text using a digital filter. Contrary to the many methods for this conversion, it is also possible for linguistic faults to appear occasionally, gender recognition, speech recognition that is unsuccessful (cannot recognize voice), and gender recognition to fail. Since technical problems are involved, we developed a program that acts as a mediator to prevent initiating software issues in order to eliminate even this little deviation. Its planned MFCC and HMM are in sync with its AI system. As a result, technical errors have been avoided.

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Section: Literature Surveymentioning

confidence: 99%