Internal Language Model

Internal language models (ILMs) are increasingly used to improve the performance of automatic speech recognition (ASR) and other sequence-to-sequence tasks by explicitly modeling the language inherent in the model's predictions. Current research focuses on refining ILM training methods within various architectures, such as neural transducers and attention-based encoder-decoder models, often employing techniques like adaptive permutation and joint training with the main model to enhance efficiency and accuracy. These advancements aim to reduce reliance on external language models, leading to faster, more robust, and adaptable systems for applications ranging from speech recognition to scene text recognition, particularly beneficial in low-resource or cross-domain scenarios.