[HTML][HTML] Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition
Recently, speech emotion recognition (SER) has become an active research area in speech
processing, particularly with the advent of deep learning (DL). Numerous DL-based methods …
processing, particularly with the advent of deep learning (DL). Numerous DL-based methods …
SERVER: Multi-modal speech emotion recognition using transformer-based and vision-based embeddings
This paper proposes a multi-modal approach for speech emotion recognition (SER) using
both text and audio inputs. The audio embedding is extracted by using a vision-based …
both text and audio inputs. The audio embedding is extracted by using a vision-based …
Multi-modal speech emotion recognition: Improving accuracy through fusion of vggish and bert features with multi-head attention
Recent research has shown that multi-modal learning is a successful method for enhancing
classification performance by mixing several forms of input, notably in speech-emotion …
classification performance by mixing several forms of input, notably in speech-emotion …
SER-Fuse: An Emotion Recognition Application Utilizing Multi-Modal, Multi-Lingual, and Multi-Feature Fusion
Speech emotion recognition (SER) is a crucial aspect of affective computing and human-
computer interaction, yet effectively identifying emotions in different speakers and languages …
computer interaction, yet effectively identifying emotions in different speakers and languages …