A novel Adaptive Weighted Transfer Subspace Learning Method for Cross-Database Speech Emotion Recognition Article Swipe
Keke Zhao
,
Peng Song
,
Shaokai Li
,
Wenjing Zhang
,
Wenming Zheng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1587/transinf.2022edl8021
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1587/transinf.2022edl8021
In this letter, we present an adaptive weighted transfer subspace learning (AWTSL) method for cross-database speech emotion recognition (SER), which can efficiently eliminate the discrepancy between source and target databases. Specifically, on one hand, a subspace projection matrix is first learned to project the cross-database features into a common subspace. At the same time, each target sample can be represented by the source samples by using a sparse reconstruction matrix. On the other hand, we design an adaptive weighted matrix learning strategy, which can improve the reconstruction contribution of important features and eliminate the negative influence of redundant features. Finally, we conduct extensive experiments on four benchmark databases, and the experimental results demonstrate the efficacy of the proposed method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1587/transinf.2022edl8021
- https://www.jstage.jst.go.jp/article/transinf/E105.D/9/E105.D_2022EDL8021/_pdf
- OA Status
- diamond
- Cited By
- 1
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294640251
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- OpenAlex ID
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https://openalex.org/W4294640251Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1587/transinf.2022edl8021Digital Object Identifier
- Title
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A novel Adaptive Weighted Transfer Subspace Learning Method for Cross-Database Speech Emotion RecognitionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-31Full publication date if available
- Authors
-
Keke Zhao, Peng Song, Shaokai Li, Wenjing Zhang, Wenming ZhengList of authors in order
- Landing page
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https://doi.org/10.1587/transinf.2022edl8021Publisher landing page
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https://www.jstage.jst.go.jp/article/transinf/E105.D/9/E105.D_2022EDL8021/_pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.jstage.jst.go.jp/article/transinf/E105.D/9/E105.D_2022EDL8021/_pdfDirect OA link when available
- Concepts
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Subspace topology, Computer science, Benchmark (surveying), Projection (relational algebra), Artificial intelligence, Pattern recognition (psychology), Transfer of learning, Speech recognition, Machine learning, Algorithm, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sparse | 67 |
| abstract_inverted_index.speech | 15 |
| abstract_inverted_index.target | 28, 55 |
| abstract_inverted_index.(AWTSL) | 11 |
| abstract_inverted_index.between | 25 |
| abstract_inverted_index.conduct | 101 |
| abstract_inverted_index.emotion | 16 |
| abstract_inverted_index.improve | 84 |
| abstract_inverted_index.learned | 40 |
| abstract_inverted_index.letter, | 2 |
| abstract_inverted_index.matrix. | 69 |
| abstract_inverted_index.method. | 118 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.project | 42 |
| abstract_inverted_index.results | 111 |
| abstract_inverted_index.samples | 63 |
| abstract_inverted_index.Finally, | 99 |
| abstract_inverted_index.adaptive | 6, 77 |
| abstract_inverted_index.efficacy | 114 |
| abstract_inverted_index.features | 45, 90 |
| abstract_inverted_index.learning | 10, 80 |
| abstract_inverted_index.negative | 94 |
| abstract_inverted_index.proposed | 117 |
| abstract_inverted_index.subspace | 9, 35 |
| abstract_inverted_index.transfer | 8 |
| abstract_inverted_index.weighted | 7, 78 |
| abstract_inverted_index.benchmark | 106 |
| abstract_inverted_index.eliminate | 22, 92 |
| abstract_inverted_index.extensive | 102 |
| abstract_inverted_index.features. | 98 |
| abstract_inverted_index.important | 89 |
| abstract_inverted_index.influence | 95 |
| abstract_inverted_index.redundant | 97 |
| abstract_inverted_index.strategy, | 81 |
| abstract_inverted_index.subspace. | 49 |
| abstract_inverted_index.databases, | 107 |
| abstract_inverted_index.databases. | 29 |
| abstract_inverted_index.projection | 36 |
| abstract_inverted_index.demonstrate | 112 |
| abstract_inverted_index.discrepancy | 24 |
| abstract_inverted_index.efficiently | 21 |
| abstract_inverted_index.experiments | 103 |
| abstract_inverted_index.recognition | 17 |
| abstract_inverted_index.represented | 59 |
| abstract_inverted_index.contribution | 87 |
| abstract_inverted_index.experimental | 110 |
| abstract_inverted_index.Specifically, | 30 |
| abstract_inverted_index.cross-database | 14, 44 |
| abstract_inverted_index.reconstruction | 68, 86 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.42378429 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |