Team Members Dr.P.Subashini, Professor, Dept of Computer Science

Dr.P.Prabhusundhar, Assistant professor,Dept of Computer Science,Gobi Arts college

Dr.R.Janani,Research Assistant, CMLI

Ms.Komalavalli .R, II MCA , Gibi Arts College

Project Summary  The project methodology comprises several essential modules aimed at developing a proficient Automatic Speech Recognition (ASR) system tailored to the nuances of the Irula language. Initially, the data collection module gathers diverse audio recordings of spoken Irula from native speakers, ensuring a comprehensive dataset representative of various dialects and speech patterns. Subsequently, the data preprocessing phase optimizes the collected data by reducing noise, normalizing signals, and segmenting audio files for efficient feature extraction. Feature extraction transforms raw audio signals into a compact and informative feature space, enabling the acoustic model to discern speech patterns accurately. Leveraging Hidden Markov Models (HMM), the acoustic model processes the extracted features to identify and differentiate Irula speech sounds among background noise. Complementing this, the language model, enhanced through pre-trained GPT models and fine-tuning on Irula language data, provides crucial linguistic context for precise speech recognition. Finally, the integration of the Streamlit framework facilitates the development of an intuitive web application interface, ensuring accessibility and ease of use for Irula speakers interacting with the ASR system. Through the seamless integration of these modules, the project aims to create a robust ASR solution that effectively bridges the language gap within the Irula community, facilitating improved communication and societal integration.