Conversion of Bengali speech to text using long short-term memory(LSTM)

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

书目详细资料
Main Authors:	Chowdhury, Mohammad Fahim, Sultana, Zakia, Jahan, Nusrat, Alavi, Safkat Hasin
其他作者:	Parvez, Mohammad Zavid
格式:	Thesis
语言:	English
出版:	Brac University 2021
主题:	Long-term memory
在线阅读:	http://hdl.handle.net/10361/15435

id	10361-15435
record_format	dspace
spelling	10361-154352022-01-26T10:20:09Z Conversion of Bengali speech to text using long short-term memory(LSTM) Chowdhury, Mohammad Fahim Sultana, Zakia Jahan, Nusrat Alavi, Safkat Hasin Parvez, Mohammad Zavid Ahmed, Sajjad Department of Computer Science and Engineering, Brac University Long-term memory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-49). Speech to text conversion is a remarkable topic in the field of Artificial Intelligence which is undoubtedly a significant medium of expressing human feelings and thoughts. However, if we compare it with text to speech, work in speech to text conversion has been done less. Among those works, many languages got priority but the numerical value of work in Bengali language is little. Previously a similar work has been done in that language where they got 82.35% accuracy using LSTM[15]. Our approach was to avail more accuracy in speech to text conversion using Neural Network models. We build a novel dataset for research purposes. We tried both GRU and LSTM and focused on LSTM later on. The reason behind it is, GRU showed an unstable and started fluctuating where LSTM is much more stable and minimized errors in case of loss function and the accuracy was also less compared to LSTM. An increasing number of datasets was giving better accuracy and on the whole dataset, the accuracy on testing data is around 90%. In terms of loss function, testing loss is less than 40%. We did data testing manually to justify the result with the given output and we got 90% accuracy rate in a dataset which the model never fed before. In the future, we would like to work with automatic sentence recognition, the process of preparing the response basis of the statement, and also changing sentiment depending on it. Mohammad Fahim Chowdhury Zakia Sultana Nusrat Jahan Safkat Hasin Alavi B. Computer Science 2021-10-19T06:35:39Z 2021-10-19T06:35:39Z 2021 2021-01 Thesis ID 17101293 ID 17301182 ID 17101332 ID 17101515 http://hdl.handle.net/10361/15435 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 49 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Long-term memory
spellingShingle	Long-term memory Chowdhury, Mohammad Fahim Sultana, Zakia Jahan, Nusrat Alavi, Safkat Hasin Conversion of Bengali speech to text using long short-term memory(LSTM)
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Parvez, Mohammad Zavid
author_facet	Parvez, Mohammad Zavid Chowdhury, Mohammad Fahim Sultana, Zakia Jahan, Nusrat Alavi, Safkat Hasin
format	Thesis
author	Chowdhury, Mohammad Fahim Sultana, Zakia Jahan, Nusrat Alavi, Safkat Hasin
author_sort	Chowdhury, Mohammad Fahim
title	Conversion of Bengali speech to text using long short-term memory(LSTM)
title_short	Conversion of Bengali speech to text using long short-term memory(LSTM)
title_full	Conversion of Bengali speech to text using long short-term memory(LSTM)
title_fullStr	Conversion of Bengali speech to text using long short-term memory(LSTM)
title_full_unstemmed	Conversion of Bengali speech to text using long short-term memory(LSTM)
title_sort	conversion of bengali speech to text using long short-term memory(lstm)
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15435
work_keys_str_mv	AT chowdhurymohammadfahim conversionofbengalispeechtotextusinglongshorttermmemorylstm AT sultanazakia conversionofbengalispeechtotextusinglongshorttermmemorylstm AT jahannusrat conversionofbengalispeechtotextusinglongshorttermmemorylstm AT alavisafkathasin conversionofbengalispeechtotextusinglongshorttermmemorylstm
_version_	1814309286521602048

Conversion of Bengali speech to text using long short-term memory(LSTM)

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