What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Machine learning
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 most cited work include:
- Opensmile: the munich versatile and fast open-source audio feature extractor (1251 citations)
- Recent developments in openSMILE, the munich open-source multimedia feature extractor (800 citations)
- New Avenues in Opinion Mining and Sentiment Analysis (712 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (51.58%)
- Speech recognition (49.34%)
- Pattern recognition (16.92%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (51.58%)
- Speech recognition (49.34%)
- Deep learning (11.62%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild (40 citations)
- The INTERSPEECH 2020 Computational Paralinguistics Challenge: Elderly Emotion, Breathing & Masks. (28 citations)
- COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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.
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