A supportive environment is vital for overall cognitive development in children. Challenges with direct observation and limitations of access to data driven approaches often hinder teachers or practitioners in early childhood research to modify or enhance classroom structures. Deploying sensor based tools in naturalistic preschool classrooms will thereby help teachers/practitioners to make informed decisions and better support student learning needs. In this study, two elements of eco-behavioral assessment: conversational speech and real-time location are fused together. While various challenges remain in developing Automatic Speech Recognition systems for spontaneous preschool children speech, efforts are made to develop a hybrid ASR engine reporting an effective Word-Error-Rate of 40%. The ASR engine further supports recognition of spoken words, WH-words, and verbs in various activity learning zones in a naturalistic preschool classroom scenario. Activity areas represent various locations within the physical ecology of an early childhood setting, each of which is suited for knowledge and skill enhancement in young children. Capturing children’s communication engagement in such areas could help teachers/practitioners fine-tune their daily activities, without the need for direct observation. This investigation provides evidence of the use of speech technology in educational settings to better support such early childhood intervention.