Language Correction Assistant using Natural Language Processing for Conversational Skill Improvement in Children

Speech enhancement, a critical component in various applications including voice assistants and telecommunication systems, aims to improve the quality and intelligibility of spoken content. This paper introduces a novel approach leveraging Natural Language Processing (NLP) techniques for speech enhancement. By harnessing the power of NLP models, we propose a methodology that not only considers the acoustic features but also incorporates linguistic context in the enhancement process. The study encompasses data collection, preprocessing, and the application of advanced NLP algorithms to extract relevant linguistic information. The experimental results demonstrate a significant improvement in speech quality, as evidenced by a substantial increase in Signal-to-Noise Ratio (SNR) and Mean Opinion Score (MOS) compared to conventional methods. Furthermore, a comparative analysis with existing techniques showcases the superiority of our approach. This research not only contributes to the advancement of speech enhancement technology but also underscores the pivotal role of NLP in this domain. The findings pave the way for future research directions, suggesting potential applications in areas such as voice recognition systems and real-time communication platforms.