Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven...
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MDPI AG
2022-05-01
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Online Access: | https://www.mdpi.com/2076-3417/12/11/5463 |
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author | Prommy Sultana Hossain Amitabha Chakrabarty Kyuheon Kim Md. Jalil Piran |
author_facet | Prommy Sultana Hossain Amitabha Chakrabarty Kyuheon Kim Md. Jalil Piran |
author_sort | Prommy Sultana Hossain |
collection | DOAJ |
description | Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well. |
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format | Article |
id | doaj.art-e261f25a60514cd9996bdef4d6b9cc18 |
institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T01:31:06Z |
publishDate | 2022-05-01 |
publisher | MDPI AG |
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spelling | doaj.art-e261f25a60514cd9996bdef4d6b9cc182023-11-23T13:42:08ZengMDPI AGApplied Sciences2076-34172022-05-011211546310.3390/app12115463Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech RecognitionPrommy Sultana Hossain0Amitabha Chakrabarty1Kyuheon Kim2Md. Jalil Piran3Department of Computer Science and Engineering, Brac University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, Brac University, Dhaka 1212, BangladeshMedia Laboratory, Kyung Hee University, Yong-in 17104, KoreaDepartment of Computer Science and Engineering, Sejeong University, Seoul 05006, KoreaExtensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well.https://www.mdpi.com/2076-3417/12/11/5463Bangla regional speech classificationStacked Convolution Autoencoder (SCAE)Multi-Label Extreme Learning machine (MLELMs)Mel Frequency Energy Coefficients (MFECs) |
spellingShingle | Prommy Sultana Hossain Amitabha Chakrabarty Kyuheon Kim Md. Jalil Piran Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition Applied Sciences Bangla regional speech classification Stacked Convolution Autoencoder (SCAE) Multi-Label Extreme Learning machine (MLELMs) Mel Frequency Energy Coefficients (MFECs) |
title | Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition |
title_full | Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition |
title_fullStr | Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition |
title_full_unstemmed | Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition |
title_short | Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition |
title_sort | multi label extreme learning machine mlelms for bangla regional speech recognition |
topic | Bangla regional speech classification Stacked Convolution Autoencoder (SCAE) Multi-Label Extreme Learning machine (MLELMs) Mel Frequency Energy Coefficients (MFECs) |
url | https://www.mdpi.com/2076-3417/12/11/5463 |
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