Review of Recent Advancement in Natural Language Processing for Automatic Speech Recognition

Automatic Speech Recognition (ASR) has undergone a transformative evolution in recent years, driven by groundbreaking advancements in Natural Language Processing (NLP). This review paper provides a comprehensive overview of the latest developments in NLP-driven ASR, focusing on key technologies such as transformer-based models, self- supervised learning, end-to-end systems, and multimodal approaches. These innovations have significantly enhanced the accuracy, efficiency, and robustness of ASR systems, enabling their application across diverse domains, including healthcare, education, and entertainment. The paper highlights the impact of models like Whisper, Wav2Vec, and Conformer, which leverage transformer architectures and self- supervised learning to achieve state-of-the-art performance. It also explores the shift from traditional pipeline-based systems to end-to- end models, which streamline the ASR process and improve scalability. Additionally, the integration of multimodal data, such as audiovisual inputs, has further expanded the capabilities of ASR systems, particularly in noisy environments and complex realworld scenarios.