An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach
Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jayanthi, K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of information technology - [Singapore] : Springer Singapore, 2017, 14(2022), 7 vom: 23. Feb., Seite 3401-3411 |
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| Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:7 ; day:23 ; month:02 ; pages:3401-3411 |
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| DOI / URN: |
10.1007/s41870-022-00900-5 |
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| Katalog-ID: |
SPR048865796 |
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| 245 | 1 | 3 | |a An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
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| 520 | |a Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. | ||
| 650 | 4 | |a Emotion recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Convolutional neural network |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a 2D CNN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Classifier fusion |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mohan, S. |4 aut | |
| 700 | 1 | |a B, Lakshmipriya |0 (orcid)0000-0002-4460-6915 |4 aut | |
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10.1007/s41870-022-00900-5 doi (DE-627)SPR048865796 (SPR)s41870-022-00900-5-e DE-627 ger DE-627 rakwb eng Jayanthi, K. verfasserin aut An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 Mohan, S. aut B, Lakshmipriya (orcid)0000-0002-4460-6915 aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2022), 7 vom: 23. Feb., Seite 3401-3411 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2022 number:7 day:23 month:02 pages:3401-3411 https://dx.doi.org/10.1007/s41870-022-00900-5 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_266 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_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_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 2022 7 23 02 3401-3411 |
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10.1007/s41870-022-00900-5 doi (DE-627)SPR048865796 (SPR)s41870-022-00900-5-e DE-627 ger DE-627 rakwb eng Jayanthi, K. verfasserin aut An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 Mohan, S. aut B, Lakshmipriya (orcid)0000-0002-4460-6915 aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2022), 7 vom: 23. Feb., Seite 3401-3411 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2022 number:7 day:23 month:02 pages:3401-3411 https://dx.doi.org/10.1007/s41870-022-00900-5 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_266 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_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_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 2022 7 23 02 3401-3411 |
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10.1007/s41870-022-00900-5 doi (DE-627)SPR048865796 (SPR)s41870-022-00900-5-e DE-627 ger DE-627 rakwb eng Jayanthi, K. verfasserin aut An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 Mohan, S. aut B, Lakshmipriya (orcid)0000-0002-4460-6915 aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2022), 7 vom: 23. Feb., Seite 3401-3411 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2022 number:7 day:23 month:02 pages:3401-3411 https://dx.doi.org/10.1007/s41870-022-00900-5 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_266 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_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_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 2022 7 23 02 3401-3411 |
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10.1007/s41870-022-00900-5 doi (DE-627)SPR048865796 (SPR)s41870-022-00900-5-e DE-627 ger DE-627 rakwb eng Jayanthi, K. verfasserin aut An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 Mohan, S. aut B, Lakshmipriya (orcid)0000-0002-4460-6915 aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2022), 7 vom: 23. Feb., Seite 3401-3411 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2022 number:7 day:23 month:02 pages:3401-3411 https://dx.doi.org/10.1007/s41870-022-00900-5 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_266 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_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_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 2022 7 23 02 3401-3411 |
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10.1007/s41870-022-00900-5 doi (DE-627)SPR048865796 (SPR)s41870-022-00900-5-e DE-627 ger DE-627 rakwb eng Jayanthi, K. verfasserin aut An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 Mohan, S. aut B, Lakshmipriya (orcid)0000-0002-4460-6915 aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2022), 7 vom: 23. Feb., Seite 3401-3411 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2022 number:7 day:23 month:02 pages:3401-3411 https://dx.doi.org/10.1007/s41870-022-00900-5 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_266 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_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_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 2022 7 23 02 3401-3411 |
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Jayanthi, K. @@aut@@ Mohan, S. @@aut@@ B, Lakshmipriya @@aut@@ |
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Jayanthi, K. |
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Jayanthi, K. misc Emotion recognition misc Convolutional neural network misc 1D CNN misc 2D CNN misc Classifier fusion An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
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An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach Emotion recognition (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 1D CNN (dpeaa)DE-He213 2D CNN (dpeaa)DE-He213 Classifier fusion (dpeaa)DE-He213 |
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An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
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Jayanthi, K. |
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integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
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An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
| abstract |
Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 |
| abstractGer |
Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 |
| abstract_unstemmed |
Abstract Research on emotion recognition is witnessing an increasing trend in recent years due to escalation in stress level of humans. Human emotions in most cases are identified through facial expressions and speech modulation. Most works in literature recognizes the emotion either from the facial recognition or through speech modulation. This work on the other hand proposes an integrated framework considering both the human static facial images and speech modulation to recognize the mental state of the individual. The proposed integrated framework by the virtue of deep classifier fusion demonstrates an exemplary performance with 94.26% accuracy on comparison with 89% and 91.49% respectively for voice signal and facial expression when considered individually. Furthermore, auto-suggestion is provided for the depressed persons to enhance their mental wellness thereby assisting them to come out of depression. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 |
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An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach |
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https://dx.doi.org/10.1007/s41870-022-00900-5 |
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Mohan, S. B, Lakshmipriya |
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| doi_str |
10.1007/s41870-022-00900-5 |
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2024-07-03T21:57:24.841Z |
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