Abstract
This survey paper examines recent advancements in speech recognition technologies and their integration with Natural Language Processing (NLP). The study begins by discussing the evolution of voice recognition systems, highlighting the transition from classical methods to deep learning models. We detail how Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer architectures have set new benchmarks in speech-to-text conversion. The second section addresses the challenges classical NLP models face in interpreting spoken language, emphasizing the need for innovative approaches to tackle dialectal variations, colloquial expressions, and context-dependent nuances. The paper also explores the potential of contextual information, multitask learning, and transfer learning to enhance voice recognition systems.