Abstract
This research focuses on the development of a Sinhala speech recognition engine tailored to identify the language content of conversations with children. The engine leverages machine learning algorithms and natural language processing (NLP) techniques to transcribe and classify speech in Sinhala. Key features include an acoustic model optimized for the nuances of Sinhala phonetics and a language model trained on datasets encompassing texts of child-directed speech. The system evaluates linguistic aspects to assess the appropriateness of content and engagement levels in child-centric dialogues. By addressing challenges such as phoneme variation and informal conversational patterns, the system aims to enhance the understanding and facilitation of Sinhala-based child interactions, promoting effective communication and developmental support.