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 ...

There has been a lot of previous works on speech emotion with machine learning method. However, most of them rely on the effectiveness of labelled speech data. In this paper, we propose a novel ...

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) , ...

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 ...

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 ...

The growing use of emotion recognition AI is causing alarm among ethicists. They warn that the tech is prone to racial biases, doesn’t account for cultural differences, and is used for mass ...

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 ...

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 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 ...

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.