CONTEXT-AWARE AUTOMATIC SPEECH RECOGNITION: INTEGRATING NLP FOR ENHANCED TRANSCRIPTION AND SUMMARIZATION

Abstract

The creation of a contemporary, improved automatic speech recognition system utilizing NLP approaches is presented in this research article. Accurate speech-to-text systems are becoming crucial for a variety of sectors and everyday applications in today's information-driven, fast-paced environment. The capacity to automatically transcribe and comprehend spoken language is essential for everything from virtual assistants and transcription services to customer service and accessibility solutions. The following essential needs in the contemporary global environment serve as the driving forces behind our project: Demand for precise voice recognition, contextual comprehension for organic dialogues, effective information processing and summary, and accessibility for a range of audiences has increased. From being able to react to a small number of sounds to being able to comprehend spoken language with ease, automatic speech recognition (ASR) has advanced dramatically. The desire to automate human-machine interaction has generated a lot of interest in this technological breakthrough. Nowadays, voice search, virtual assistants, and speech-to-text systems are just a few of the many applications that employ ASR extensively, greatly improving user experience and productivity. As evidence of the amazing progress made in this area, it started with simple sound recognition and has since progressed to complete language understanding. A proposed solution to this problem is the integration of Natural Language Processing (NLP) methods into ASR systems. ASR systems can improve their capacity to identify and comprehend spoken language in its larger context by including contextual understanding features obtained from NLP models.