Abstract
Natural Language Processing is an area of artificial intelligent that concentrates on the interaction between computers and human languages. The application tailored specifically for underrepresented languages spoken in Malawi. Recognizing the linguistic diversity and the digital divide faced by many Malawian communities. The system will utilize deep learning-based speech recognition engines and NLP techniques to improve speech recognition accuracy and provide a user friendly interface for speech-to-text transcription. The project aims to promote preservation of local dialect, enhance communication, and increase accessibility to digital communication for the country. It also to bridge the digital language divide by enabling voice-based interactions and documentation in local languages. The system is trained using a curated dataset of local dialect speech samples, annotated and preprocessed to enhance phonetic and linguistic accuracy. The application is designed for mobile and desktop environments, offering a user-friendly interface and real-time speech recognition capabilities. The proposed system will have significant implications for education, healthcare, public service delivery in multilingual communities and economic development for Malawi. The proposed system leverages machine learning and deep learning techniques, specifically automatic speech recognition models combined with natural language understanding modules. It utilizes acoustic modeling, phoneme recognition, and language modeling to capture the unique pronunciation patterns, tonal variation, and syntactic structures of the target dialect. A dataset comprising recorded speech samples from native speakers is collected and processed for training and testing purposes. Advanced models such as recurrent neural networks and transformers-based architectures (for example Wav2vec2)are applied to enhance accuracy and robustness against background noise and speaker variability.