Speech to Text Translation

Speech-to-text translation (STT) aims to automatically convert spoken language into written text in a different language, focusing on improving accuracy, efficiency, and robustness. Current research emphasizes developing efficient transformer-based models, including variations like linearized transformers and monotonic attention mechanisms, to handle long sequences and real-time translation (simultaneous STT). A key trend involves tackling more realistic scenarios, such as multi-speaker conversations and noisy audio, often through end-to-end models incorporating speaker diarization or leveraging audio-visual data. These advancements are crucial for creating more practical and versatile STT systems with applications ranging from multilingual communication to accessibility technologies.