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The convergence of Text-to-Speech (TTS) and Speech-to-Text (STT) models has spearheaded an era of transformative advancements in natural language processing (NLP). This article discusses transformative advances in natural language processing (NLP). It presents a comprehensive survey that systematically examines specific STT and TTS models, such as rule-based systems and deep learning architectures, and explores their structures, methodologies, and applications. The paper classifies these models based on their underlying technologies, architectural approaches, training methods, and application domains, and provides a detailed overview of their development. It highlights industry successes and challenges, covering a range of application areas, from assistive technologies to virtual assistants and language translation. This research provides new perspectives to understand the current world of STT and TTS technologies, which will especially benefit researchers, developers, and industry players. By highlighting the empowering bridge between written and spoken language, the paper uniquely contributes to the ongoing dialogue in the field. It not only sheds light on the current state of STT and TTS technologies but also lays the groundwork for future innovations and advances that could change the way we interact with language.
Sudhan et al. (Fri,) studied this question.