Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters

The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural...

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Autor principal:	Ji-Yeoun Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	elderly voice analysis deep learning classifier skewness kurtosis medical IT Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/aab94a242b2f46feb6426015713ef431
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spelling	oai:doaj.org-article:aab94a242b2f46feb6426015713ef4312021-11-11T14:58:31ZClassification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters10.3390/app112198362076-3417https://doaj.org/article/aab94a242b2f46feb6426015713ef4312021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9836https://doaj.org/toc/2076-3417The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. In total, 126 subjects (63 elderly and 63 young) were obtained from the Saarbruecken voice database. The highest performance of 93.75% appeared when the skewness was added to the MFCC and MFCC delta parameters, although the fusion of the skewness and kurtosis parameters had a positive effect on the overall accuracy of the classification. The results of this study also revealed that the performance of FNN was higher than that of CNN. Most parameters estimated from male data samples demonstrated good performance in terms of gender. Rather than using mixed female and male data, this work recommends the development of separate systems that represent the best performance through each optimized parameter using data from independent male and female samples.Ji-Yeoun LeeMDPI AGarticleelderly voice analysisdeep learning classifierskewnesskurtosismedical ITTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9836, p 9836 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	elderly voice analysis deep learning classifier skewness kurtosis medical IT Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	elderly voice analysis deep learning classifier skewness kurtosis medical IT Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ji-Yeoun Lee Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
description	The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. In total, 126 subjects (63 elderly and 63 young) were obtained from the Saarbruecken voice database. The highest performance of 93.75% appeared when the skewness was added to the MFCC and MFCC delta parameters, although the fusion of the skewness and kurtosis parameters had a positive effect on the overall accuracy of the classification. The results of this study also revealed that the performance of FNN was higher than that of CNN. Most parameters estimated from male data samples demonstrated good performance in terms of gender. Rather than using mixed female and male data, this work recommends the development of separate systems that represent the best performance through each optimized parameter using data from independent male and female samples.
format	article
author	Ji-Yeoun Lee
author_facet	Ji-Yeoun Lee
author_sort	Ji-Yeoun Lee
title	Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
title_short	Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
title_full	Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
title_fullStr	Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
title_full_unstemmed	Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters
title_sort	classification between elderly voices and young voices using an efficient combination of deep learning classifiers and various parameters
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/aab94a242b2f46feb6426015713ef431
work_keys_str_mv	AT jiyeounlee classificationbetweenelderlyvoicesandyoungvoicesusinganefficientcombinationofdeeplearningclassifiersandvariousparameters
_version_	1718437919101812736

Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters

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