The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
Open accessAbstract
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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Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938
Self-citationWöllmer, M., Eyben, F., Reiter, S. A., Schuller, B. W., Cox, C., Douglas‐Cowie, E., & Cowie, R. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-192 · Cited by 333
Self-citationSchuller, B. W., Müller, R., Eyben, F., Gast, J., Hörnler, B., Wöllmer, M., Rigoll, G., Hoethker, A., & Konosu, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.02.013 · Cited by 194
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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-citationA review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Speech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 826
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-citationCOVAREP — A collaborative voice analysis repository for speech technologies
Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014) · 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2014.6853739 · Cited by 695
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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