Rare Word

Rare word recognition in automatic speech recognition (ASR) and machine translation (MT) remains a significant challenge, hindering performance in various applications. Current research focuses on improving model robustness to rare words through techniques like incorporating external knowledge sources (dictionaries, retrieved examples), modifying model architectures (conformers, transducers), and employing data-driven biasing strategies during training and decoding. These advancements aim to enhance the accuracy and reliability of speech and text processing systems, particularly in low-resource scenarios and specialized domains where rare words are prevalent, ultimately improving user experience and the effectiveness of downstream tasks.