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pro vyhledávání: '"Schuller, Björn"'
In this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rathe
Externí odkaz:
http://arxiv.org/abs/2405.13206
Autor:
Rajapakshe, Thejan, Rana, Rajib, Khalifa, Sara, Sisman, Berrak, Schuller, Bjorn W., Busso, Carlos
Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the performance of SER models has significantly improved. However, designin
Externí odkaz:
http://arxiv.org/abs/2403.14083
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistical
Externí odkaz:
http://arxiv.org/abs/2308.11578
The goal of Speech Emotion Recognition (SER) is to enable computers to recognize the emotion category of a given utterance in the same way that humans do. The accuracy of SER is strongly dependent on the validity of the utterance-level representation
Externí odkaz:
http://arxiv.org/abs/2303.05134
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel
Externí odkaz:
http://arxiv.org/abs/2209.14345
Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved.
Externí odkaz:
http://arxiv.org/abs/2207.12248
Autor:
Watkins, Edward R *, Warren, Fiona C, Newbold, Alexandra, Hulme, Claire, Cranston, Timothy, Aas, Benjamin, Bear, Holly, Botella, Cristina, Burkhardt, Felix, Ehring, Thomas, Fazel, Mina, Fontaine, Johnny R J, Frost, Mads, Garcia-Palacios, Azucena, Greimel, Ellen, Hößle, Christiane, Hovasapian, Arpine, Huyghe, Veerle E I, Karpouzis, Kostas, Löchner, Johanna, Molinari, Guadalupe, Pekrun, Reinhard, Platt, Belinda, Rosenkranz, Tabea, Scherer, Klaus R, Schlegel, Katja, Schuller, Bjorn W, Schulte-Korne, Gerd, Suso-Ribera, Carlos, Voigt, Varinka, Voß, Maria, Taylor, Rod S
Publikováno v:
In The Lancet Digital Health December 2024 6(12):e904-e913
Autor:
Watkins, Edward R *, Warren, Fiona C, Newbold, Alexandra, Hulme, Claire, Cranston, Timothy, Aas, Benjamin, Bear, Holly, Botella, Cristina, Burkhardt, Felix, Ehring, Thomas, Fazel, Mina, Fontaine, Johnny R J, Frost, Mads, Garcia-Palacios, Azucena, Greimel, Ellen, Hößle, Christiane, Hovasapian, Arpine, Huyghe, Veerle E I, Karpouzis, Kostas, Löchner, Johanna, Molinari, Guadalupe, Pekrun, Reinhard, Platt, Belinda, Rosenkranz, Tabea, Scherer, Klaus R, Schlegel, Katja, Schuller, Bjorn W, Schulte-Korne, Gerd, Suso-Ribera, Carlos, Voigt, Varinka, Voß, Maria, Taylor, Rod S
Publikováno v:
In The Lancet Digital Health December 2024 6(12):e894-e903
Professional athletes increasingly use automated analysis of meta- and signal data to improve their training and game performance. As in other related human-to-human research fields, signal data, in particular, contain important performance- and mood
Externí odkaz:
http://arxiv.org/abs/2202.09102
Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart
Externí odkaz:
http://arxiv.org/abs/2012.04650