DBT: multimodal emotion recognition based on dual-branch transformer
Abstract There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yi, Yufan [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 Springer Science+Business Media, LLC, part of Springer Nature 2022. 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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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 79(2022), 8 vom: 21. Dez., Seite 8611-8633 |
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| Übergeordnetes Werk: |
volume:79 ; year:2022 ; number:8 ; day:21 ; month:12 ; pages:8611-8633 |
| Links: |
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| DOI / URN: |
10.1007/s11227-022-05001-5 |
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| Katalog-ID: |
SPR049955497 |
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| 520 | |a Abstract There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. | ||
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| 650 | 4 | |a Adaptive interlayer fusion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Weighted label smoothing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Weighted DS strategy |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tian, Yan |4 aut | |
| 700 | 1 | |a He, Cong |4 aut | |
| 700 | 1 | |a Fan, Yajing |4 aut | |
| 700 | 1 | |a Hu, Xinli |4 aut | |
| 700 | 1 | |a Xu, Yiping |4 aut | |
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10.1007/s11227-022-05001-5 doi (DE-627)SPR049955497 (SPR)s11227-022-05001-5-e DE-627 ger DE-627 rakwb eng Yi, Yufan verfasserin aut DBT: multimodal emotion recognition based on dual-branch transformer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 Tian, Yan aut He, Cong aut Fan, Yajing aut Hu, Xinli aut Xu, Yiping aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 8 vom: 21. Dez., Seite 8611-8633 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:8 day:21 month:12 pages:8611-8633 https://dx.doi.org/10.1007/s11227-022-05001-5 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_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_206 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_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 79 2022 8 21 12 8611-8633 |
| spelling |
10.1007/s11227-022-05001-5 doi (DE-627)SPR049955497 (SPR)s11227-022-05001-5-e DE-627 ger DE-627 rakwb eng Yi, Yufan verfasserin aut DBT: multimodal emotion recognition based on dual-branch transformer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 Tian, Yan aut He, Cong aut Fan, Yajing aut Hu, Xinli aut Xu, Yiping aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 8 vom: 21. Dez., Seite 8611-8633 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:8 day:21 month:12 pages:8611-8633 https://dx.doi.org/10.1007/s11227-022-05001-5 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_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_206 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_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 79 2022 8 21 12 8611-8633 |
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10.1007/s11227-022-05001-5 doi (DE-627)SPR049955497 (SPR)s11227-022-05001-5-e DE-627 ger DE-627 rakwb eng Yi, Yufan verfasserin aut DBT: multimodal emotion recognition based on dual-branch transformer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 Tian, Yan aut He, Cong aut Fan, Yajing aut Hu, Xinli aut Xu, Yiping aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 8 vom: 21. Dez., Seite 8611-8633 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:8 day:21 month:12 pages:8611-8633 https://dx.doi.org/10.1007/s11227-022-05001-5 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_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_206 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_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 79 2022 8 21 12 8611-8633 |
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10.1007/s11227-022-05001-5 doi (DE-627)SPR049955497 (SPR)s11227-022-05001-5-e DE-627 ger DE-627 rakwb eng Yi, Yufan verfasserin aut DBT: multimodal emotion recognition based on dual-branch transformer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 Tian, Yan aut He, Cong aut Fan, Yajing aut Hu, Xinli aut Xu, Yiping aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 8 vom: 21. Dez., Seite 8611-8633 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:8 day:21 month:12 pages:8611-8633 https://dx.doi.org/10.1007/s11227-022-05001-5 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_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_206 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_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 79 2022 8 21 12 8611-8633 |
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10.1007/s11227-022-05001-5 doi (DE-627)SPR049955497 (SPR)s11227-022-05001-5-e DE-627 ger DE-627 rakwb eng Yi, Yufan verfasserin aut DBT: multimodal emotion recognition based on dual-branch transformer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 Tian, Yan aut He, Cong aut Fan, Yajing aut Hu, Xinli aut Xu, Yiping aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 8 vom: 21. Dez., Seite 8611-8633 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:8 day:21 month:12 pages:8611-8633 https://dx.doi.org/10.1007/s11227-022-05001-5 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_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_206 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_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 79 2022 8 21 12 8611-8633 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR049955497</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230406064824.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230406s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-022-05001-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049955497</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11227-022-05001-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yi, Yufan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">DBT: multimodal emotion recognition based on dual-branch transformer</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. 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Yi, Yufan |
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DBT: multimodal emotion recognition based on dual-branch transformer wav2vec2.0 (dpeaa)DE-He213 Model fine-tuning (dpeaa)DE-He213 Adaptive interlayer fusion (dpeaa)DE-He213 Weighted label smoothing (dpeaa)DE-He213 Weighted DS strategy (dpeaa)DE-He213 |
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dbt: multimodal emotion recognition based on dual-branch transformer |
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DBT: multimodal emotion recognition based on dual-branch transformer |
| abstract |
Abstract There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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 There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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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DBT: multimodal emotion recognition based on dual-branch transformer |
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https://dx.doi.org/10.1007/s11227-022-05001-5 |
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| author2 |
Tian, Yan He, Cong Fan, Yajing Hu, Xinli Xu, Yiping |
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Tian, Yan He, Cong Fan, Yajing Hu, Xinli Xu, Yiping |
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10.1007/s11227-022-05001-5 |
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2024-07-04T02:54:53.177Z |
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| score |
7.401354 |