RNN Transducer

Recurrent Neural Network Transducers (RNN-Ts) are end-to-end models for sequence-to-sequence tasks, primarily used in automatic speech recognition (ASR) for their accuracy and streaming capabilities. Current research focuses on improving RNN-T efficiency through faster decoding algorithms (like greedy decoding on GPUs), architectural modifications (such as incorporating duration prediction or multi-blank symbols), and novel training methods (including alignment-free training and lattice-free discriminative training) to enhance speed and accuracy. These advancements are significant because they address limitations in computational cost and latency, making RNN-Ts more suitable for real-time and resource-constrained applications, while also improving accuracy and robustness in challenging scenarios like multi-talker speech.