2018 - IEEE Fellow For contributions to computer audition
2017 - ACM Senior Member
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Affective computing, Natural language processing and Pattern recognition. Björn Schuller combines subjects such as Artificial neural network, Recurrent neural network and Valence with his study of Speech recognition. He focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Multi-task learning.
His work carried out in the field of Affective computing brings together such families of science as Cognitive psychology, Facial expression and Affect. His work in Natural language processing addresses subjects such as Speaker recognition, which are connected to disciplines such as Mixture model. His study in the fields of Discriminative model and Mel-frequency cepstrum under the domain of Pattern recognition overlaps with other disciplines such as Matrix decomposition.
His scientific interests lie mostly in Artificial intelligence, Speech recognition, Pattern recognition, Machine learning and Feature extraction. In most of his Artificial intelligence studies, his work intersects topics such as Natural language processing. His biological study spans a wide range of topics, including Valence and Recurrent neural network.
Björn Schuller performs integrative Pattern recognition and Non-negative matrix factorization research in his work. Many of his studies involve connections with topics such as Task and Machine learning. His study focuses on the intersection of Emotion recognition and fields such as Affective computing with connections in the field of Cognitive psychology and Affect.
Björn Schuller focuses on Artificial intelligence, Speech recognition, Deep learning, Machine learning and Task. The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing and Pattern recognition. His Speech recognition research integrates issues from Recurrent neural network, Feature extraction and Feature learning.
His Machine learning study integrates concerns from other disciplines, such as Annotation, Variety, Task analysis and Sound classification. His research integrates issues of Facial expression and Human–computer interaction in his study of Task. Björn Schuller has researched Feature in several fields, including Context and Feature vector.
Björn Schuller mainly focuses on Artificial intelligence, Deep learning, Speech recognition, Machine learning and Computer audition. His Artificial intelligence research incorporates elements of Natural language processing and Pattern recognition. The various areas that Björn Schuller examines in his Deep learning study include mHealth, Wearable computer, Usability, Human–computer interaction and Multimodality.
Björn Schuller combines subjects such as End-to-end principle, Feature extraction and Breathing with his study of Speech recognition. In general Machine learning study, his work on Transfer of learning often relates to the realm of Mathematical proof, thereby connecting several areas of interest. His work carried out in the field of Computer audition brings together such families of science as Autism and Cognitive science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Opensmile: the munich versatile and fast open-source audio feature extractor
Florian Eyben;Martin Wöllmer;Björn Schuller.
acm multimedia (2010)
New Avenues in Opinion Mining and Sentiment Analysis
E. Cambria;B. Schuller;Yunqing Xia;C. Havasi.
IEEE Intelligent Systems (2013)
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Florian Eyben;Felix Weninger;Florian Gross;Björn Schuller.
acm multimedia (2013)
The INTERSPEECH 2009 Emotion Challenge
Björn W. Schuller;Stefan Steidl;Anton Batliner.
conference of the international speech communication association (2009)
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
Björn Schuller;Anton Batliner;Stefan Steidl;Dino Seppi.
Speech Communication (2011)
Hidden Markov model-based speech emotion recognition
B. Schuller;G. Rigoll;M. Lang.
international conference on acoustics, speech, and signal processing (2003)
The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism
Björn W. Schuller;Stefan Steidl;Anton Batliner;Alessandro Vinciarelli.
conference of the international speech communication association (2013)
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Florian Eyben;Klaus R. Scherer;Bjorn W. Schuller;Johan Sundberg.
IEEE Transactions on Affective Computing (2016)
The INTERSPEECH 2010 Paralinguistic Challenge
Björn W. Schuller;Stefan Steidl;Anton Batliner;Felix Burkhardt.
conference of the international speech communication association (2010)
Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network
George Trigeorgis;Fabien Ringeval;Raymond Brueckner;Erik Marchi.
international conference on acoustics, speech, and signal processing (2016)
Profile was last updated on December 6th, 2021.
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