Direct S2ST

Direct speech-to-speech translation (S2ST) aims to translate spoken language directly from one language to another without an intermediate text representation, prioritizing speed and naturalness. Current research focuses on improving model architectures, such as transformer-based and cascaded models, often incorporating techniques like discrete speech units and multi-task learning to enhance accuracy and efficiency, particularly in low-resource scenarios and on-device applications. These advancements are significant for improving cross-lingual communication, particularly in real-time applications like simultaneous interpretation and facilitating human-computer interaction. Addressing privacy concerns through techniques like preset-voice matching is also a growing area of focus.