Age group classification and gender recognition from speech with temporal convolutional neural networks

Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discr...
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Autor*in:	Sánchez-Hevia, Héctor A. [verfasserIn] Gil-Pita, Roberto Utrilla-Manso, Manuel Rosa-Zurera, Manuel

Format:	Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Interactive voice response Age estimation Gender recognition Human-robot interaction Machine learning

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 3 vom: Jan., Seite 3535-3552
Übergeordnetes Werk:	volume:81 ; year:2022 ; number:3 ; month:01 ; pages:3535-3552

Links:	Volltext

DOI / URN:	10.1007/s11042-021-11614-4

Katalog-ID:	OLC2078099147

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allfields	10.1007/s11042-021-11614-4 doi (DE-627)OLC2078099147 (DE-He213)s11042-021-11614-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sánchez-Hevia, Héctor A. verfasserin aut Age group classification and gender recognition from speech with temporal convolutional neural networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. Interactive voice response Age estimation Gender recognition Human-robot interaction Machine learning Gil-Pita, Roberto aut Utrilla-Manso, Manuel aut Rosa-Zurera, Manuel (orcid)0000-0002-3073-3278 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 3 vom: Jan., Seite 3535-3552 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:3 month:01 pages:3535-3552 https://doi.org/10.1007/s11042-021-11614-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 3 01 3535-3552
spelling	10.1007/s11042-021-11614-4 doi (DE-627)OLC2078099147 (DE-He213)s11042-021-11614-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sánchez-Hevia, Héctor A. verfasserin aut Age group classification and gender recognition from speech with temporal convolutional neural networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. Interactive voice response Age estimation Gender recognition Human-robot interaction Machine learning Gil-Pita, Roberto aut Utrilla-Manso, Manuel aut Rosa-Zurera, Manuel (orcid)0000-0002-3073-3278 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 3 vom: Jan., Seite 3535-3552 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:3 month:01 pages:3535-3552 https://doi.org/10.1007/s11042-021-11614-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 3 01 3535-3552
allfields_unstemmed	10.1007/s11042-021-11614-4 doi (DE-627)OLC2078099147 (DE-He213)s11042-021-11614-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sánchez-Hevia, Héctor A. verfasserin aut Age group classification and gender recognition from speech with temporal convolutional neural networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. Interactive voice response Age estimation Gender recognition Human-robot interaction Machine learning Gil-Pita, Roberto aut Utrilla-Manso, Manuel aut Rosa-Zurera, Manuel (orcid)0000-0002-3073-3278 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 3 vom: Jan., Seite 3535-3552 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:3 month:01 pages:3535-3552 https://doi.org/10.1007/s11042-021-11614-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 3 01 3535-3552
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author	Sánchez-Hevia, Héctor A.
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title_sort	age group classification and gender recognition from speech with temporal convolutional neural networks
title_auth	Age group classification and gender recognition from speech with temporal convolutional neural networks
abstract	Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. © The Author(s) 2021
abstractGer	Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. © The Author(s) 2021
abstract_unstemmed	Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. © The Author(s) 2021
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title_short	Age group classification and gender recognition from speech with temporal convolutional neural networks
url	https://doi.org/10.1007/s11042-021-11614-4
remote_bool	false
author2	Gil-Pita, Roberto Utrilla-Manso, Manuel Rosa-Zurera, Manuel
author2Str	Gil-Pita, Roberto Utrilla-Manso, Manuel Rosa-Zurera, Manuel
ppnlink	189064145
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11042-021-11614-4
up_date	2024-07-03T18:48:47.905Z
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