Sequence Transducer

Sequence transducers are neural network architectures designed for efficient and accurate sequence-to-sequence mapping, primarily used in speech recognition and other time-series processing tasks. Current research focuses on improving efficiency through techniques like frame-level criteria, optimized decoding algorithms (e.g., label-looping), and model compression methods (e.g., knowledge distillation), while also exploring novel architectures such as Conformer-T and CIF-T to enhance performance and reduce latency. These advancements are significant because they enable faster, more accurate, and resource-efficient applications in areas like speech recognition, machine translation, and recommendation systems, particularly on resource-constrained devices.