Eyben, Florian

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

Music and Audio ProcessingSpeech and Audio Processing

Cited by 2,642

The 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

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 1,818

OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit

Eyben, F., Wollmer, M., & Schuller, B. (2009) · 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 418

Deep neural networks for acoustic emotion recognition: Raising the benchmarks

Stuhlsatz, A., Meyer, C., Eyben, F., Zielke, T., Meier, G., & Schuller, B. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 288

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 288

The INTERSPEECH 2012 speaker trait challenge

Schuller, B., Steidl, S., Batliner, A., Nöth, E., Vinciarelli, A., Burkhardt, F., Son, R. V., Weninger, F., Eyben, F., Bocklet, T., et al. (2012) · Interspeech 2012

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 211

Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data

Ringeval, F., Eyben, F., Kroupi, E., Yuce, A., Thiran, J.-P., Ebrahimi, T., Lalanne, D., & Schuller, B. (2015) · Pattern Recognition Letters

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 202

Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling

Wöllmer, M., Metallinou, A., Eyben, F., Schuller, B., & Narayanan, S. S. (2010) · Interspeech 2010

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 201

Building autonomous sensitive artificial listeners

Schroder, M., Bevacqua, E., Cowie, R., Eyben, F., Gunes, H., Heylen, D., ter Maat, M., McKeown, G., Pammi, S., Pantic, M., et al. (2012) · IEEE Transactions on Affective Computing

Speech and dialogue systemsEmotion and Mood Recognition

Cited by 185

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B. (2022) · Frontiers in Digital Health

Voice and Speech DisordersEmotion and Mood Recognition

Cited by 78