The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
Open accessAbstract
As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: Acted data is often used rather than spontaneous data, results are reported on preselected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only a little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by interset evaluation: We therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful for learning about chances to add resources for training and overcoming the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter to intracorpus testing.
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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2457417 · Cited by 1,818
Self-citationEmotion recognition using multi-modal data and machine learning techniques: A tutorial and review
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Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
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Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition
Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.90 · Cited by 366
Self-citationDeep neural networks for acoustic emotion recognition: Raising the benchmarks
Stuhlsatz, A., Meyer, C., Eyben, F., Zielke, T., Meier, G., & Schuller, B. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2011.5947651 · Cited by 288
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Metadata
- Authors
- Schuller, B., Vlasenko, B., Eyben, F., Wollmer, M., Stuhlsatz, A., Wendemuth, A., & Rigoll, G.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2010-07
- Volume
- 1
- Issue
- 2
- Pages
- 119-131
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2125462608
- Cited by
- 388
- References
- 92
- FWCI
- 17.58
- Type
- journal article
- Language
- en
- Open access
- green
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