Neural Transducer

Neural transducers are end-to-end sequence-to-sequence models primarily used for automatic speech recognition (ASR) and increasingly for tasks like speech translation and text-to-speech. Current research focuses on improving their efficiency and accuracy through techniques like self-supervised pre-training, factorized model architectures (e.g., incorporating separate language models), and novel training strategies such as sequence discriminative training and blank-symbol regularization. These advancements aim to create faster, more accurate, and resource-efficient models with applications in various fields, including improved voice assistants, real-time translation systems, and more robust speech processing in noisy environments.