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pro vyhledávání: '"Schuller, A."'
Emotion and Intent Joint Understanding in Multimodal Conversation (MC-EIU) aims to decode the semantic information manifested in a multimodal conversational history, while inferring the emotions and intents simultaneously for the current utterance. M
Externí odkaz:
http://arxiv.org/abs/2407.02751
Uncertainty Quantification (UQ) is an important building block for the reliable use of neural networks in real-world scenarios, as it can be a useful tool in identifying faulty predictions. Speech emotion recognition (SER) models can suffer from part
Externí odkaz:
http://arxiv.org/abs/2407.01143
Autor:
Christ, Lukas, Amiriparian, Shahin, Hawighorst, Friederike, Schill, Ann-Kathrin, Boutalikakis, Angelo, Graf-Vlachy, Lorenz, König, Andreas, Schuller, Björn W.
Flattery is an important aspect of human communication that facilitates social bonding, shapes perceptions, and influences behavior through strategic compliments and praise, leveraging the power of speech to build rapport effectively. Its automatic d
Externí odkaz:
http://arxiv.org/abs/2406.17667
Autor:
Chang, Yi, Ren, Zhao, Zhao, Zhonghao, Nguyen, Thanh Tam, Qian, Kun, Schultz, Tanja, Schuller, Björn W.
Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and com
Externí odkaz:
http://arxiv.org/abs/2406.15119
Autor:
Chen, Tian-Yue, Ren, Haowen, Ghazikhanian, Nareg, Hage, Ralph El, Sasaki, Dayne Y., Salev, Pavel, Takamura, Yayoi, Schuller, Ivan K., Kent, Andrew D.
Metal-insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in s
Externí odkaz:
http://arxiv.org/abs/2406.11679
Foundation models have shown great promise in speech emotion recognition (SER) by leveraging their pre-trained representations to capture emotion patterns in speech signals. To further enhance SER performance across various languages and domains, we
Externí odkaz:
http://arxiv.org/abs/2406.10275
Autor:
Amiriparian, Shahin, Christ, Lukas, Kathan, Alexander, Gerczuk, Maurice, Müller, Niklas, Klug, Steffen, Stappen, Lukas, König, Andreas, Cambria, Erik, Schuller, Björn, Eulitz, Simone
The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes o
Externí odkaz:
http://arxiv.org/abs/2406.07753
In ornithology, bird species are known to have variedit's widely acknowledged that bird species display diverse dialects in their calls across different regions. Consequently, computational methods to identify bird species onsolely through their call
Externí odkaz:
http://arxiv.org/abs/2406.08517
Contrastive language-audio pretraining (CLAP) has recently emerged as a method for making audio analysis more generalisable. Specifically, CLAP-style models are able to `answer' a diverse set of language queries, extending the capabilities of audio m
Externí odkaz:
http://arxiv.org/abs/2406.07203
The expression of emotion is highly individualistic. However, contemporary speech emotion recognition (SER) systems typically rely on population-level models that adopt a `one-size-fits-all' approach for predicting emotion. Moreover, standard evaluat
Externí odkaz:
http://arxiv.org/abs/2406.06665