English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traditional approaches in speech emotion recognition, such as LSTM, CNN, RNN,SVM, and MLP, have limitations such as difficulty capturing long-termdependencies in sequential data, capturing the temporal dynamics, andstruggling to capture complex patterns and relationships in multimodal data.This research addresses these shortcomings by proposing an ensemble model thatcombines Graph Convolutional Networks (GCN) for processing textual data and theHuBERT transformer for analyzing audio signals. We found that GCNs excel atcapturing Long-term contextual dependencies and relationships within textualdata by leveraging graph-based representations of text and thus detecting thecontextual meaning and semantic relationships between words. On the other hand,HuBERT utilizes self-attention mechanisms to capture long-range dependencies,enabling the modeling of temporal dynamics present in speech and capturingsubtle nuances and variations that contribute to emotion recognition. Bycombining GCN and HuBERT, our ensemble model can leverage the strengths of bothapproaches. This allows for the simultaneous analysis of multimodal data, andthe fusion of these modalities enables the extraction of complementaryinformation, enhancing the discriminative power of the emotion recognitionsystem. The results indicate that the combined model can overcome thelimitations of traditional methods, leading to enhanced accuracy in recognizingemotions from speech.

Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques