Assessing child growth in terms of speech and language development is a critical indicator of long term learning ability and life-long development progress. The earlier a child who is at-risk is identified, the earlier support can be provided to reduce the social impact of the speech or language issue. The preschool classroom provides an opportunity for monitoring growth in young children’s interactions. To date, limited research has been possible for young child based speech recognition in classroom settings due to speech data access, as well as limitations on speech recognition performance for naturalistic child communication. This study addresses American English speech recognition for children’s speech in a naturalistic noisy early childhood setting, where child age varies from 3 to 5 years. This study investigates the effectiveness of data augmentation techniques to improve both language and acoustic models, since this is relatively under explored for young child speech. We consider alternate text augmentation approaches using adult data, Web data, and text generated by recurrent neural networks. We also compare several acoustic augmentation techniques including: speed perturbation, tempo perturbation, and adult data. In the study, we also comment on child word count rates to assess child speech development. Finally, insights are provided into the statistical patterns of naturalistic child speech such as word complexity, stop words, part of speech, etc., which are intended to serve as a representative of high quality language engagement in adult–child learning environments.