Recurrent Neural Network Transducer

Recurrent Neural Network Transducers (RNN-Ts) are a prominent architecture for end-to-end automatic speech recognition (ASR), aiming to improve accuracy and efficiency in streaming applications. Current research focuses on optimizing RNN-T models for various constraints, including low latency, reduced memory footprint (through techniques like binarization and knowledge distillation), and robustness to noisy data or diverse acoustic conditions. These advancements are significant for deploying accurate and efficient ASR systems on resource-limited devices and improving the performance of various speech-related applications, such as voice assistants and conversational AI.