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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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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 ›
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