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OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
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Various open-source toolkits exist for speech recognition and speech processing. These toolkits have brought a great benefit to the research community, i.e. speeding up research. Yet, no such freely available toolkit exists for automatic affect recognition from speech. We herein introduce a novel open-source affect and emotion recognition engine, which integrates all necessary components in one highly efficient software package. The components include audio recording and audio file reading, state-of-the-art paralinguistic feature extraction and plugable classification modules. In this paper we introduce the engine and extensive baseline results. Pre-trained models for four affect recognition tasks are included in the openEAR distribution. The engine is tailored for multi-threaded, incremental on-line processing of live input in real-time, however it can also be used for batch processing of databases.
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“In current MER, it is common practice to use OpenSMILE [45], COVAREP [46], OpenEAR [47], and librosa [48] to extract the aforementioned acoustic features that represent the emotional characteristics of the speech modality.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [47]
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Amato, F., Saggio, G., Cesarini, V., Olmo, G., & Costantini, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119651 · Cited by 52
Medium-term speaker states—A review on intoxication, sleepiness and the first challenge
Schuller, B. W., Steidl, S., Batliner, A. M., Schiel, F., Krajewski, J., Weninger, F. J., & Eyben, F. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.12.002 · Cited by 55
Self-citationSpeech Emotion Recognition via an Attentive Time–Frequency Neural Network
Lu, C., Zheng, W., Lian, H., Zong, Y., Tang, C., Li, S., & Zhao, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3219825 · Cited by 51
“For instance, Schuller et al. [11] integrated 6552 hand-crafted features into an open-source toolkit, i.e., open-EAR, for four affective recognition tasks and then extended the openEAR to a multifunctional and convenient toolkit for diverse speech-related tasks, i.e., openSMILE [12].”
BackgroundMethodReal-life emotion-related states detection in call centers: a cross-corpora study
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Deep Learning Models for Speech Emotion Recognition
Praseetha, V. M., & Vadivel, S. (2018) · Journal of Computer Science · doi:10.3844/jcssp.2018.1577.1587 · Cited by 49
Automatic classification of married couples' behavior using audio features
Black, M., Katsamanis, A., Lee, C.-C., Lammert, A. C., Baucom, B., Christensen, A., Georgiou, P., & Narayanan, S. S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-574 · Cited by 50
Yogesh, C. K., Hariharan, M., Ngadiran, R., Adom, A. H., Yaacob, S., & Polat, K. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.013 · Cited by 50
Mao, Q., Xu, G., Xue, W., Gou, J., & Zhan, Y. (2017) · Speech Communication · doi:10.1016/j.specom.2017.06.006 · Cited by 48
Voice attributes affecting likability perception
Weiß, B., & Burkhardt, F. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-570 · Cited by 46
Metadata
- Authors
- Eyben, F., Wollmer, M., & Schuller, B.
- Affiliations
- Technical University of Munich (DE)
- ORCID
- Eyben, Florian
- Venue
- 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1-6
- ISSN
- 2156-8103 (ISSN-L)
- OpenAlex
- W2167854178
- Cited by
- 418
- References
- 18
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 20 · 2023: 21 · 2022: 20 · 2021: 14 · 2020: 21 · 2019: 30 · 2018: 18 · 2017: 31 · 2016: 24 · 2015: 35 · 2014: 25 · 2013: 36 · 2012: 27
- FWCI
- 62.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- 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 › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- affect recognition, emotion recognition, speech emotion recognition, paralinguistic features, open-source software toolkit, real-time speech processing, audio recordings, affective computing
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