Conformer Model

Conformer models, a type of neural network architecture combining convolutional and self-attention mechanisms, are increasingly used for various speech and audio processing tasks, aiming to improve accuracy and efficiency. Current research focuses on optimizing conformer models for specific applications like automatic speech recognition (ASR), speech separation, and seizure detection, often incorporating techniques like federated learning and quantization to address resource constraints and improve robustness. These advancements have led to significant improvements in performance across diverse applications, impacting fields such as healthcare (e.g., seizure prediction) and human-computer interaction (e.g., improved speech recognition systems).