Combining Efforts for Improving Automatic Classification of Emotional User States
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This record contains a full paper presented at the 5th Conference on Language Technologies (JT-2006), held in Ljubljana, Slovenia, in October 2006.
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CEICES experiments used a pool of more than 4,000 acoustic and linguistic features for emotion recognition.
“A cooperation of different sites under the name CEICES (Combining Efforts for Improving Automatic Classification of Emotional user States) has carried out experiments based on a pool of more than 4,000 features, including acoustic and linguistic feature types [3].”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [3]
The Introduction describes CEICES as a collaboration across sites to classify emotional user states and says the sites shared features covering acoustic and linguistic phenomena. The Abstract also describes combined classification using features from the sites. Neither passage establishes that the feature pool exceeded 4,000; searches for “4,000” and “4000” found no supporting count in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAmong acoustic features, duration and energy are most relevant, while voice quality has less impact.
“Among acoustic features, duration and energy seem to be most relevant, while voice quality showed less impact.”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [3]
The Introduction says the study assessed acoustic and linguistic features, but the passages available from our copy do not rank duration, energy, and voice quality. Searches for those terms and for feature relevance found no passage establishing the claimed comparison; no page numbers were provided. (LLM, conf=0.70)
Experimental — accuracy not yet measuredNo single feature group outperformed the pool of all acoustic features.
“Yet, no single group outperformed the pool of all acoustic features.”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [3]
The Abstract (page unavailable) says that combining the best features from participating sites improved classification, and the Introduction says the study considered linguistic as well as acoustic features. Searches for individual acoustic feature groups and a pooled all-acoustic comparison found no passage reporting the comparison claimed. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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What's wrong with Bonferroni adjustments
Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900 · see citing works
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Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642 · see citing works
Self-citationThe 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 · see citing works
Self-citationThe INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938 · see citing works
Self-citationAutomatic speech emotion recognition using modulation spectral features
Wu, S., Falk, T. H., & Chan, W.-Y. (2011) · Speech Communication · doi:10.1016/j.specom.2010.08.013 · Cited by 389 · see citing works
Fear-type emotion recognition for future audio-based surveillance systems
Clavel, C., Vasilescu, I., Devillers, L., Richard, G., & Ehrette, T. (2008) · Speech Communication · doi:10.1016/j.specom.2008.03.012 · Cited by 180 · see citing works
Self-citationExploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
Wagner, J., Andre, E., Lingenfelser, F., & Kim, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.12 · Cited by 156 · see citing works
Tandem decoding of children's speech for keyword detection in a child-robot interaction scenario
Wöllmer, M., Schuller, B., Batliner, A., Steidl, S., & Seppi, D. (2011) · ACM Transactions on Speech and Language Processing · doi:10.1145/1998384.1998386 · Cited by 18 · see citing works
Self-citation
- Authors
- Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu, L., Amir, N., Kessous, L., & Aharonson, V.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Technical University of Munich (DE)
- Istituto per la Ricerca Scientifica e Tecnologica (IT)
- Karlsruhe Institute of Technology (DE)
- University of Augsburg (DE)
- Centre National de la Recherche Scientifique (FR)
- Tel Aviv University (IL)
- Department of Communication Disorders, Sackler Faculty of Medicine, Tel Aviv University
- Afeka College of Engineering (IL)
- ORCID
- Venue
- Jožef Stefan Institute, Ljubljana
- Publisher
- Jožef Stefan Institute, Ljubljana
- Published
- 2006
- OpenAlex
- W2747342956
- Cited by
- 119
- References
- 1
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 2 · 2019: 2 · 2018: 2 · 2017: 4 · 2016: 2 · 2015: 9 · 2014: 3 · 2013: 13 · 2012: 6
- FWCI
- 14.33
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research)
- KITopen (other-oa)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- multimodal emotion recognition
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