Advances and challenges in developing high-accuracy language models for automatic speech recognition (ASR) systems

This article examines the advances and challenges in developing high-accuracy language models for automatic speech recognition (ASR) systems. It discusses deep learning and neural network-based approaches, as well as the interaction between acoustic and language models. The study highlights key challenges such as limited training data, variability in accents and speech patterns, background noise, and real-time processing requirements. The findings provide insights into improving the accuracy and efficiency of ASR systems.