One Pas Multiple Conformer

One-pass multiple conformer research aims to optimize the Conformer architecture, a hybrid convolutional-transformer model, for efficient and robust speech processing across various tasks. Current efforts focus on enhancing Conformer's performance in long-form speech recognition, multilingual applications, and noisy environments through techniques like incorporating state-space models, multiple convolution kernels, and efficient attention mechanisms. These advancements are significant for improving the speed, accuracy, and resource efficiency of automatic speech recognition, speech separation, and other audio-visual processing applications, impacting both research and practical deployment of speech technology.