Gender classification in Bangla language using deep learning-based voice analysis

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Библиографические подробности
Главные авторы:	Hakim, Talukder Juhaer, Monsur, Sayema Binte, Shuvo, Abtahi Maskawath, Azrine, Tasmia, Labib, Md. Zarif
Другие авторы:	Alam, Md. Ashraful
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	Deep learning Machine learning Bangla language F1-score Decision tree Inception V3 DenseNet-121 STFT MFCC Neural networks (Computer science) Cognitive learning theory (Deep learning) Automatic speech recognition > Data processing
Online-ссылка:	http://hdl.handle.net/10361/23636

id	10361-23636
record_format	dspace
spelling	10361-236362024-07-03T05:09:15Z Gender classification in Bangla language using deep learning-based voice analysis Hakim, Talukder Juhaer Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Labib, Md. Zarif Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Deep learning Machine learning Bangla language F1-score Decision tree Inception V3 DenseNet-121 STFT MFCC Neural networks (Computer science) Cognitive learning theory (Deep learning) Automatic speech recognition--Data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-50). Gender classification based on voice analysis is one of the elemental tasks in speech and audio processing, with various applications such as speech recognition systems, voice assistants, call center analytics, etc. For speech synthesis, speaker identification, and human-computer interaction- gender recognition plays a vital role. Although extensive research on this topic has been done in various languages, any studies can hardly be found regarding gender classification in the Bangla language. Our research paper aims to recognize gender in the Bangla language using deep learning approaches and voice analysis. The core of our approach involves the use of CNN models (ResNet50, EfficientNetB0, InceptionV3, and DenseNet-121) for our data training. The Mel-Frequency Cepstral Coefficients (MFCC) and short-time Fourier transforms (STFT) were computed from audio recordings and used as input features to the neural network model. The system’s excellent accuracy rate demonstrates its potential for use in practical settings. By providing light on the application of deep learning techniques in the context of the Bangla language, this study advances the area of gender identification. 95% accuracy was achieved in the InspectionV3 and EfficientNetB0 models with the MFCC input. Talukder Juhaer Hakim Sayema Binte Monsur Abtahi Maskawath Shuvo Tasmia Azrine Md. Zarif Labib B.Sc. in Computer Science 2024-07-02T06:20:57Z 2024-07-02T06:20:57Z ©2023 2023 Thesis ID 19301134 ID 19301030 ID 19301131 ID 20301165 ID 19301165 http://hdl.handle.net/10361/23636 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. 59 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Machine learning Bangla language F1-score Decision tree Inception V3 DenseNet-121 STFT MFCC Neural networks (Computer science) Cognitive learning theory (Deep learning) Automatic speech recognition--Data processing
spellingShingle	Deep learning Machine learning Bangla language F1-score Decision tree Inception V3 DenseNet-121 STFT MFCC Neural networks (Computer science) Cognitive learning theory (Deep learning) Automatic speech recognition--Data processing Hakim, Talukder Juhaer Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Labib, Md. Zarif Gender classification in Bangla language using deep learning-based voice analysis
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Alam, Md. Ashraful
author_facet	Alam, Md. Ashraful Hakim, Talukder Juhaer Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Labib, Md. Zarif
format	Thesis
author	Hakim, Talukder Juhaer Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Labib, Md. Zarif
author_sort	Hakim, Talukder Juhaer
title	Gender classification in Bangla language using deep learning-based voice analysis
title_short	Gender classification in Bangla language using deep learning-based voice analysis
title_full	Gender classification in Bangla language using deep learning-based voice analysis
title_fullStr	Gender classification in Bangla language using deep learning-based voice analysis
title_full_unstemmed	Gender classification in Bangla language using deep learning-based voice analysis
title_sort	gender classification in bangla language using deep learning-based voice analysis
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23636
work_keys_str_mv	AT hakimtalukderjuhaer genderclassificationinbanglalanguageusingdeeplearningbasedvoiceanalysis AT monsursayemabinte genderclassificationinbanglalanguageusingdeeplearningbasedvoiceanalysis AT shuvoabtahimaskawath genderclassificationinbanglalanguageusingdeeplearningbasedvoiceanalysis AT azrinetasmia genderclassificationinbanglalanguageusingdeeplearningbasedvoiceanalysis AT labibmdzarif genderclassificationinbanglalanguageusingdeeplearningbasedvoiceanalysis
_version_	1814307020387385344

Gender classification in Bangla language using deep learning-based voice analysis

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