Language Adaptive

Language adaptation in machine learning focuses on enabling models to efficiently handle multiple languages, particularly in low-resource scenarios where data for certain languages is scarce. Current research emphasizes techniques like encoder prompting, self-supervised learning, and sparse sharing sub-networks to improve model adaptability across diverse languages, often within multilingual speech recognition and text-to-speech systems. These advancements are crucial for broadening the accessibility and effectiveness of AI applications, particularly in areas like cross-lingual communication and information retrieval.