Recognizing human emotions by machines is a complex task. Deep learning models
attempt to automate this process by rendering machines to exhibit learning capabilities …

Since Multimodal Emotion Recognition in Conversation (MERC) can be applied to public
opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive …

Paralinguistic speech processing is important in addressing many issues, such as sentiment
and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable …

The motive of these investigations is to present the numerical performances of the fractional
order mathematical influenza disease model (FO-MIDM) by designing the computational …

In this paper, an automatic speech emotion recognition (SER) task of classifying eight
different emotions was experimented using parallel based networks trained using the …

Modality alignment can maintain the consistency of semantics in multi-modal emotion
recognition tasks, ensuring that features from different modalities accurately represent the …

Multi-modal emotion recognition is crucial for human-computer interaction. Many existing
algorithms attempt to achieve multi-modal interactions through a cross-attention mechanism …

The last decade has seen an exponential rise in the number of attention-based models for
speech emotion recognition (SER). Most of these models use a spectrogram as the input …

Speech emotion recognition (SER) is of great importance in human-computer interaction.
Recent research has demonstrated that self-supervised learned acoustic and linguistic …

Speech emotion recognition (SER) has received increased attention due to its extensive
applications in many fields, especially in the analysis of teacher-student dialogue in …