Conformer Encoder

Conformer encoders are a type of neural network architecture increasingly used in automatic speech recognition (ASR) and related tasks, aiming to improve accuracy and efficiency by combining convolutional and self-attention mechanisms. Current research focuses on addressing computational limitations of self-attention, enhancing robustness to noisy data and privacy concerns through techniques like differential privacy and model pruning, and optimizing the encoder for various scenarios such as different subsampling rates and streaming applications. These advancements are leading to more accurate, efficient, and privacy-preserving speech processing systems with applications in diverse fields including healthcare and voice-controlled devices.