The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The INTERSPEECH 2009 emotion challenge
Abstract
The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus [1] serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech. Index Terms: emotion, challenge, feature types, classification 1.
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“On the FAU Aibo Emotion Corpus [30], as part of the INTERSPEECH Emotion Challenge 2009 [25], we were able to slightly exceed the baseline given by the organizers for a five class problem (anger, emphatic, neutral, positive, and rest) [32].”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [25]
In the citing paper
“This number varies from several hundred [157] to several thousand [158], [123], depending on the employed LLDs and HSFs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [157]
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Cites
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Emotion recognition in human-computer interaction
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.911197 · Cited by 2,603
Speech recognition by machines and humans
Lippmann, R. P. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00021-6 · Cited by 531
The Vera am Mittag German audio-visual emotional speech database
Grimm, M., Kroschel, K., & Narayanan, S. S. (2008) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2008.4607572 · Cited by 413
Schuller, B. W., Batliner, A. M., Seppi, D., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., Kessous, L., et al. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-612 · Cited by 191
Self-citationCombining Efforts for Improving Automatic Classification of Emotional User States
Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu, L., Amir, N., Kessous, L., et al. (2006) · Jožef Stefan Institute, Ljubljana · doi:10.5281/zenodo.14167504 · Cited by 119
Self-citationA State of the Art Review on Emotional Speech Databases
Ververidis, D., & Kotropoulos, K. (2003) · Cited by 88
Audiovisual recognition of spontaneous interest within conversations
Schuller, B. W., Müeller, R., Hörnler, B., Hoethker, A., Konosu, H., & Rigoll, G. (2007) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1322192.1322201 · Cited by 85
Self-citationPatterns, prototypes, performance: classifying emotional user states
Seppi, D., Batliner, A. M., Schuller, B. W., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., & Aharonson, V. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-193 · Cited by 41
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Opensmile: the munich versatile and fast open-source audio feature extractor
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
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
Self-citationReview on Psychological Stress Detection Using Biosignals
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2927337 · Cited by 764
Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.011 · Cited by 765
Self-citationValstar, M., Schuller, B. W., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantić, M. (2013) · doi:10.1145/2512530.2512533 · Cited by 581
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Metadata
- Authors
- Schuller, B., Steidl, S., & Batliner, A.
- Affiliations
- Technical University of Munich (DE)
- MorphoSys (Germany) (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Venue
- Interspeech 2009
- Publisher
- ISCA
- Published
- 2009-09-06
- Pages
- 312-315
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W1501669607
- Cited by
- 938
- References
- 9
- Citations by year
- 2026: 34 · 2025: 35 · 2024: 25 · 2023: 38 · 2022: 50 · 2021: 77 · 2020: 84 · 2019: 79 · 2018: 80 · 2017: 64 · 2016: 64 · 2015: 56 · 2014: 53 · 2013: 47 · 2012: 40
- FWCI
- 87.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Funders
- European Commission (2007–2013, FP7 2007-2013, FP7/2007, 211486)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- emotion recognition from speech, benchmarking, spontaneous speech, room acoustics, speaker independence, feature types, emotion classification
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