English Conversation Partner Finder Using MLP Based Fluency Classifier and Linguamatch Algorithm

The ability to communicate fluently in a language is crucial in today's globalized world. Non-native speakers often face challenges in achieving fluency and finding opportunities for language practice. In this research work, we propose a novel approach to address these challenges by developing a fluency detection model and a linguistic matching algorithm. The fluency detection model utilizes machine learning techniques to assess the fluency level of non-native speakers. By analyzing a large dataset of audio recordings, the trained model can accurately classify fluency levels as low, intermediate, or high. This model can provide valuable insights into an individual's language proficiency and serve as a tool for self-assessment and improvement. Complementing the fluency detection model, there is also a linguistic matching algorithm that aims to connect non-native speakers with native speakers for language practice. By considering the user's interests and fluency level, the algorithm suggests suitable language partners, facilitating meaningful language exchanges and enhancing the learning experience. Through our research, the proposed work aims to empower non-native speakers by providing them with a comprehensive toolset for fluency assessment and language practice.