An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity of methods ...

A vector quantized masked autoencoder for speech emotion recognition Samir Sadok, Simon Leglaive, Renaud Séguier IEEE ICASSP 2023 Workshop on Self-Supervision in Audio, Speech and Beyond (SASB). If ...

Federated Multidomain Learning With Graph Ensemble Autoencoder GMM for Emotion Recognition Abstract: Facial expression cognition technology continues to face challenges from certain perspectives ...

Zheng (2017) presented an EEG emotion recognition algorithm based on group sparse canonical correlation analysis (GSCCA), which can improve the accuracy of emotion recognition. In Xing et al. (2019) , ...

In this study we are looking at this task from slightly another angle -- emotions recognition. We design a joint of convolutional and recurrent neural networks with the usage of autoencoder to ...

In emotion recognition based on physiological signals, collecting enough labeled data of a single subject for training is time-consuming and expensive. ... Liu et al. (2016) used a deep autoencoder to ...

Emotion recognition AI is bunk. Don’t get me wrong, AI that recognizes human sentiment and emotion can be very useful. For example, it can help identify when drivers are falling asleep behind ...

Jan 27 (Thomson Reuters Foundation) - Technology that measures emotions based on biometric indicators such as facial movements, tone of voice or body movements, is increasingly being marketed in ...

Emotion recognition technology typically relies on software to look at any number of qualities — facial expressions, tone of voice or word choice — to automatically detect emotional state.