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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Praat: Doing Phonetics by Computer
(2011) · Ear and Hearing · doi:10.1097/aud.0b013e31821473f7 · Cited by 8,010
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
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365
The INTERSPEECH 2009 emotion challenge
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
Self-citation
Research works printed but not linked: 1
- young, 2006, The HTK book (v3 4)
Cited by
Sorted by citations
Items 401–418 of 418
Cambria, E., & Hussain, A. (2015) · Springer eBooks · doi:10.1007/978-3-319-23654-4_3
Schuller, D., & Schuller, B. W. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_26
Self-citationBishop, J. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0034-6.ch037
Recurrent Neural Networks With Conformer for Speech Emotion RecognitionPreprint
Sun, C., Yang, J., Huang, X. M., & Hou, X. (2022) · doi:10.22541/au.166658945.58688710/v1
Sentiment Analysis Techniques and their Uses: An Analytical Study
Sikarwar, A. (2023) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon57294.2023.10111939
Hinduja, S., Darzi, A., Ertuğrul, I. Ö., Provenza, N. R., Gadot, R., Storch, E., Sheth, S. A., Goodman, W. K., & Cohn, J. F. (2023) · doi:10.36227/techrxiv.23256119.v1
Hinduja, S., Darzi, A., Ertuğrul, I. Ö., Provenza, N. R., Gadot, R., Storch, E., Sheth, S. A., Goodman, W. K., & Cohn, J. F. (2023) · doi:10.36227/techrxiv.23256119
Speech Emotion Recognition using AdaMax and Weighted Adam Optimizers
Tyagi, S., Szénási, S., & von Neumann, J. (2023) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti59972.2023.10382013
Audio Based Emotion Classification Using Classifier Ensemble
Mudassar, A., Haq, S. U., Majid, M. I., Rasool, I., & Kamran, M. (2023) · Pakistan Journal of Engineering Technology & Science · doi:10.22555/pjets.v11i2.1020
Synthesizing Speech-guided Face Animation by Low-Rank and Noise-tolerant Active Learning
Marchesoti, L. (2024) · IEEE Access · doi:10.1109/access.2024.3391301
A Research Pathway for Stance Detection in Speech
Nguyen, V., Zhang, X., & Colarik, A. M. (2023) · Global Conference on Information Technologies and Communications (GCITC) · doi:10.1109/gcitc60406.2023.10659745
Translation-Based Fusion for Multimodal Sentiment Analysis: Short Comprehensive Review
Nada, T., Yassir, K. S., Oubenaalla, Y., & Habib, N. E. (2024) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds62089.2024.10756364
Omiya, Y., Komura, M., Nakamura, M., Higuchi, M., Komura, H., & Tokuno, S. (2024) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes61011.2024.10990773
Evaluating Deep Learning Models for Negative Speech Emotion Recognition in Security Systems
Ngoc, T. H., My, T. G., Duy, T. N. H., & Nguyen, T. (2025) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf68649.2025.11365144
Analyse d'opinion dans les interactions orales
Barriere, V. (2019) · theses.fr (ABES) · doi:10.70675/d65df106z9fe1z44f0zb9bbzb1caa1590c7a
Text and Speech Emotion Recognition for Human–Computer Interaction
Condesso, S. A. L., Leite, N., & Ferreira, A. J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29653-5_12
Antonicelli, G., Morin, B. T., Bogley, R., Newbury, R., Ezzes, Z., Ramkrishnan, S., Miller, Z., Mandelli, M. L., Tee, B. L., García, A. M., et al. (2026) · Cortex · doi:10.1016/j.cortex.2026.10.006
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
