3 accepted papers
Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements across various natural language processing tasks including stance detection, however, their performance in stance detec…
The time-frequency domain remains central to the speech signal analysis. Enhancing the efficacy of neural network-based speech models demands a detailed multi-scale analysis of time-frequency features. This study presents the Hybrid Attention Time-Frequency Analysis Network (HATFANet), an innovative…
Time-frequency analysis in single-channel speech enhancement has received considerable attention. While Transformer-based architectures are gaining traction, their computational burden can be substantial, especially when dealing with longer speech samples. To address this, our research introduces th…