This research presents DESpeech, which enhances translation quality and speed in speech-to-speech translation, suggesting a robust solution for real-time communication.
Key Points
The primary aim is to improve the efficiency and quality of direct speech-to-speech translation systems.
Developed a dual-pass encoder architecture for S2ST.
Implemented acoustic feature extraction through a speech encoder.
Achieved semantic understanding via a text encoder.
Utilized discrete units as intermediate representations.
Adopted a multi-task learning framework integrating auxiliary tasks.
DESpeech outperforms existing methods in translation quality.
Shows significant improvements in inference speed.
Maintains a balance between computational efficiency and translation accuracy.