Speech to Unit

Speech-to-unit (S2U) translation focuses on converting speech into discrete, quantized representations ("units") for improved efficiency and performance in speech processing tasks. Current research emphasizes self-supervised learning and transformer-based architectures to generate these units, often within a direct speech-to-speech translation (S2ST) framework, bypassing the need for intermediate text transcription. This approach is particularly relevant for low-resource languages and applications requiring real-time translation, offering potential improvements in speed, accuracy, and multilingual capabilities for various applications like voice assistants and cross-lingual communication.