A novel multiclass classification based approach for playback attack detection in speaker verification systems

Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mankad, Sapan H. [verfasserIn] Garg, Sanjay Patel, Vansh Patwa, Nishi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multiclass classification Speaker verification Playback spoofing detection

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 14(2023), 12 vom: 17. Sept., Seite 16737-16748
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:12 ; day:17 ; month:09 ; pages:16737-16748

Links:	Volltext

DOI / URN:	10.1007/s12652-023-04684-9

Katalog-ID:	SPR054186366

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520			\|a Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario.
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allfields	10.1007/s12652-023-04684-9 doi (DE-627)SPR054186366 (SPR)s12652-023-04684-9-e DE-627 ger DE-627 rakwb eng Mankad, Sapan H. verfasserin aut A novel multiclass classification based approach for playback attack detection in speaker verification systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213 Garg, Sanjay aut Patel, Vansh aut Patwa, Nishi aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2023), 12 vom: 17. Sept., Seite 16737-16748 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748 https://dx.doi.org/10.1007/s12652-023-04684-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 12 17 09 16737-16748
spelling	10.1007/s12652-023-04684-9 doi (DE-627)SPR054186366 (SPR)s12652-023-04684-9-e DE-627 ger DE-627 rakwb eng Mankad, Sapan H. verfasserin aut A novel multiclass classification based approach for playback attack detection in speaker verification systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213 Garg, Sanjay aut Patel, Vansh aut Patwa, Nishi aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2023), 12 vom: 17. Sept., Seite 16737-16748 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748 https://dx.doi.org/10.1007/s12652-023-04684-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 12 17 09 16737-16748
allfields_unstemmed	10.1007/s12652-023-04684-9 doi (DE-627)SPR054186366 (SPR)s12652-023-04684-9-e DE-627 ger DE-627 rakwb eng Mankad, Sapan H. verfasserin aut A novel multiclass classification based approach for playback attack detection in speaker verification systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213 Garg, Sanjay aut Patel, Vansh aut Patwa, Nishi aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2023), 12 vom: 17. Sept., Seite 16737-16748 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748 https://dx.doi.org/10.1007/s12652-023-04684-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 12 17 09 16737-16748
allfieldsGer	10.1007/s12652-023-04684-9 doi (DE-627)SPR054186366 (SPR)s12652-023-04684-9-e DE-627 ger DE-627 rakwb eng Mankad, Sapan H. verfasserin aut A novel multiclass classification based approach for playback attack detection in speaker verification systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213 Garg, Sanjay aut Patel, Vansh aut Patwa, Nishi aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2023), 12 vom: 17. Sept., Seite 16737-16748 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748 https://dx.doi.org/10.1007/s12652-023-04684-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 12 17 09 16737-16748
allfieldsSound	10.1007/s12652-023-04684-9 doi (DE-627)SPR054186366 (SPR)s12652-023-04684-9-e DE-627 ger DE-627 rakwb eng Mankad, Sapan H. verfasserin aut A novel multiclass classification based approach for playback attack detection in speaker verification systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213 Garg, Sanjay aut Patel, Vansh aut Patwa, Nishi aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2023), 12 vom: 17. Sept., Seite 16737-16748 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748 https://dx.doi.org/10.1007/s12652-023-04684-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 12 17 09 16737-16748
language	English
source	Enthalten in Journal of ambient intelligence and humanized computing 14(2023), 12 vom: 17. Sept., Seite 16737-16748 volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748
sourceStr	Enthalten in Journal of ambient intelligence and humanized computing 14(2023), 12 vom: 17. Sept., Seite 16737-16748 volume:14 year:2023 number:12 day:17 month:09 pages:16737-16748
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multiclass classification Speaker verification Playback spoofing detection
isfreeaccess_bool	false
container_title	Journal of ambient intelligence and humanized computing
authorswithroles_txt_mv	Mankad, Sapan H. @@aut@@ Garg, Sanjay @@aut@@ Patel, Vansh @@aut@@ Patwa, Nishi @@aut@@
publishDateDaySort_date	2023-09-17T00:00:00Z
hierarchy_top_id	620775734
id	SPR054186366
language_de	englisch
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author	Mankad, Sapan H.
spellingShingle	Mankad, Sapan H. misc Multiclass classification misc Speaker verification misc Playback spoofing detection A novel multiclass classification based approach for playback attack detection in speaker verification systems
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topic_title	A novel multiclass classification based approach for playback attack detection in speaker verification systems Multiclass classification (dpeaa)DE-He213 Speaker verification (dpeaa)DE-He213 Playback spoofing detection (dpeaa)DE-He213
topic	misc Multiclass classification misc Speaker verification misc Playback spoofing detection
topic_unstemmed	misc Multiclass classification misc Speaker verification misc Playback spoofing detection
topic_browse	misc Multiclass classification misc Speaker verification misc Playback spoofing detection
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title	A novel multiclass classification based approach for playback attack detection in speaker verification systems
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title_full	A novel multiclass classification based approach for playback attack detection in speaker verification systems
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title_sort	novel multiclass classification based approach for playback attack detection in speaker verification systems
title_auth	A novel multiclass classification based approach for playback attack detection in speaker verification systems
abstract	Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Spoofing detection in automatic speaker verification (ASV) systems is typically handled as a binary classification approach. In this paper, we propose a novel approach to address this problem using a multi-class classification approach. Each audio sample is tagged on the basis of the source of the signal. Spoof class samples are divided according to corresponding recording devices which were used during recording of the genuine speaker’s voice to be later used for implementing playback attack. Three different multiclass based approaches proposed in this work are evaluated on ASVspoof 2017 v2.0 dataset. The performance of these systems is tested on conventional and deep classifier systems using both handcrafted features and spectrographic representations of audio. Results suggest the potential of the proposed multiclass classification based approach in comparison to binary classification, specifically in deep learning scenario. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	12
title_short	A novel multiclass classification based approach for playback attack detection in speaker verification systems
url	https://dx.doi.org/10.1007/s12652-023-04684-9
remote_bool	true
author2	Garg, Sanjay Patel, Vansh Patwa, Nishi
author2Str	Garg, Sanjay Patel, Vansh Patwa, Nishi
ppnlink	620775734
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hochschulschrift_bool	false
doi_str	10.1007/s12652-023-04684-9
up_date	2024-07-04T00:21:50.177Z
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A novel multiclass classification based approach for playback attack detection in speaker verification systems

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