Language Augmentation

Language augmentation is a technique used to enhance the performance of natural language processing (NLP) and speech processing models, particularly in low-resource scenarios or when dealing with noisy data. Current research focuses on developing effective augmentation strategies, including methods that leverage large language models (LLMs) to generate synthetic data, and exploring the impact of different augmentation types (e.g., syntactic, lexical, phonetic) on various tasks such as speech recognition, speaker recognition, and text classification. These advancements are significant because they improve the robustness and generalizability of NLP and speech models, leading to more accurate and reliable applications across diverse languages and domains.