Emotion Neural Transducer for Fine-Grained Speech Emotion Recognition Article Swipe
Siyuan Shen
,
Yu Gao
,
Feng Liu
,
Hanyang Wang
,
Aimin Zhou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/icassp48485.2024.10446974
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/icassp48485.2024.10446974
The mainstream paradigm of speech emotion recognition (SER) is identifying\nthe single emotion label of the entire utterance. This line of works neglect\nthe emotion dynamics at fine temporal granularity and mostly fail to leverage\nlinguistic information of speech signal explicitly. In this paper, we propose\nEmotion Neural Transducer for fine-grained speech emotion recognition with\nautomatic speech recognition (ASR) joint training. We first extend typical\nneural transducer with emotion joint network to construct emotion lattice for\nfine-grained SER. Then we propose lattice max pooling on the alignment lattice\nto facilitate distinguishing emotional and non-emotional frames. To adapt\nfine-grained SER to transducer inference manner, we further make blank, the\nspecial symbol of ASR, serve as underlying emotion indicator as well, yielding\nFactorized Emotion Neural Transducer. For typical utterance-level SER, our ENT\nmodels outperform state-of-the-art methods on IEMOCAP in low word error rate.\nExperiments on IEMOCAP and the latest speech emotion diarization dataset ZED\nalso demonstrate the superiority of fine-grained emotion modeling. Our code is\navailable at https://github.com/ECNU-Cross-Innovation-Lab/ENT.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icassp48485.2024.10446974
- OA Status
- green
- Cited By
- 19
- References
- 30
- Related Works
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- OpenAlex ID
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https://openalex.org/W4392903514Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icassp48485.2024.10446974Digital Object Identifier
- Title
-
Emotion Neural Transducer for Fine-Grained Speech Emotion RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-18Full publication date if available
- Authors
-
Siyuan Shen, Yu Gao, Feng Liu, Hanyang Wang, Aimin ZhouList of authors in order
- Landing page
-
https://doi.org/10.1109/icassp48485.2024.10446974Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2403.19224Direct OA link when available
- Concepts
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Computer science, Speech recognition, Utterance, Emotion classification, Emotion recognition, Artificial neural network, Leverage (statistics), Artificial intelligence, Natural language processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 7Per-year citation counts (last 5 years)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 3, 13, 19, 34, 100, 142 |
| abstract_inverted_index.on | 77, 122, 129 |
| abstract_inverted_index.to | 31, 65, 90 |
| abstract_inverted_index.we | 41, 72, 94 |
| abstract_inverted_index.For | 113 |
| abstract_inverted_index.Our | 146 |
| abstract_inverted_index.SER | 89 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 28, 84, 131 |
| abstract_inverted_index.for | 45 |
| abstract_inverted_index.low | 125 |
| abstract_inverted_index.max | 75 |
| abstract_inverted_index.our | 117 |
| abstract_inverted_index.the | 14, 78, 132, 140 |
| abstract_inverted_index.ASR, | 101 |
| abstract_inverted_index.SER, | 116 |
| abstract_inverted_index.SER. | 70 |
| abstract_inverted_index.Then | 71 |
| abstract_inverted_index.This | 17 |
| abstract_inverted_index.code | 147 |
| abstract_inverted_index.fail | 30 |
| abstract_inverted_index.fine | 25 |
| abstract_inverted_index.line | 18 |
| abstract_inverted_index.make | 96 |
| abstract_inverted_index.this | 39 |
| abstract_inverted_index.with | 61 |
| abstract_inverted_index.word | 126 |
| abstract_inverted_index.(ASR) | 53 |
| abstract_inverted_index.(SER) | 7 |
| abstract_inverted_index.error | 127 |
| abstract_inverted_index.first | 57 |
| abstract_inverted_index.joint | 54, 63 |
| abstract_inverted_index.label | 12 |
| abstract_inverted_index.serve | 102 |
| abstract_inverted_index.well, | 108 |
| abstract_inverted_index.works | 20 |
| abstract_inverted_index.Neural | 43, 111 |
| abstract_inverted_index.blank, | 97 |
| abstract_inverted_index.entire | 15 |
| abstract_inverted_index.extend | 58 |
| abstract_inverted_index.latest | 133 |
| abstract_inverted_index.mostly | 29 |
| abstract_inverted_index.paper, | 40 |
| abstract_inverted_index.signal | 36 |
| abstract_inverted_index.single | 10 |
| abstract_inverted_index.speech | 4, 35, 47, 51, 134 |
| abstract_inverted_index.symbol | 99 |
| abstract_inverted_index.Emotion | 110 |
| abstract_inverted_index.IEMOCAP | 123, 130 |
| abstract_inverted_index.dataset | 137 |
| abstract_inverted_index.emotion | 5, 11, 22, 48, 62, 67, 105, 135, 144 |
| abstract_inverted_index.frames. | 86 |
| abstract_inverted_index.further | 95 |
| abstract_inverted_index.lattice | 68, 74 |
| abstract_inverted_index.manner, | 93 |
| abstract_inverted_index.methods | 121 |
| abstract_inverted_index.network | 64 |
| abstract_inverted_index.pooling | 76 |
| abstract_inverted_index.propose | 73 |
| abstract_inverted_index.typical | 114 |
| abstract_inverted_index.dynamics | 23 |
| abstract_inverted_index.paradigm | 2 |
| abstract_inverted_index.temporal | 26 |
| abstract_inverted_index.ZED\nalso | 138 |
| abstract_inverted_index.alignment | 79 |
| abstract_inverted_index.construct | 66 |
| abstract_inverted_index.emotional | 83 |
| abstract_inverted_index.indicator | 106 |
| abstract_inverted_index.inference | 92 |
| abstract_inverted_index.modeling. | 145 |
| abstract_inverted_index.training. | 55 |
| abstract_inverted_index.Transducer | 44 |
| abstract_inverted_index.facilitate | 81 |
| abstract_inverted_index.mainstream | 1 |
| abstract_inverted_index.outperform | 119 |
| abstract_inverted_index.transducer | 60, 91 |
| abstract_inverted_index.underlying | 104 |
| abstract_inverted_index.utterance. | 16 |
| abstract_inverted_index.ENT\nmodels | 118 |
| abstract_inverted_index.Transducer. | 112 |
| abstract_inverted_index.demonstrate | 139 |
| abstract_inverted_index.diarization | 136 |
| abstract_inverted_index.explicitly. | 37 |
| abstract_inverted_index.granularity | 27 |
| abstract_inverted_index.information | 33 |
| abstract_inverted_index.lattice\nto | 80 |
| abstract_inverted_index.recognition | 6, 49, 52 |
| abstract_inverted_index.superiority | 141 |
| abstract_inverted_index.fine-grained | 46, 143 |
| abstract_inverted_index.neglect\nthe | 21 |
| abstract_inverted_index.the\nspecial | 98 |
| abstract_inverted_index.is\navailable | 148 |
| abstract_inverted_index.non-emotional | 85 |
| abstract_inverted_index.distinguishing | 82 |
| abstract_inverted_index.typical\nneural | 59 |
| abstract_inverted_index.utterance-level | 115 |
| abstract_inverted_index.with\nautomatic | 50 |
| abstract_inverted_index.identifying\nthe | 9 |
| abstract_inverted_index.propose\nEmotion | 42 |
| abstract_inverted_index.state-of-the-art | 120 |
| abstract_inverted_index.for\nfine-grained | 69 |
| abstract_inverted_index.rate.\nExperiments | 128 |
| abstract_inverted_index.adapt\nfine-grained | 88 |
| abstract_inverted_index.leverage\nlinguistic | 32 |
| abstract_inverted_index.yielding\nFactorized | 109 |
| abstract_inverted_index.https://github.com/ECNU-Cross-Innovation-Lab/ENT.\n | 150 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.99067713 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |