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Enriching the quality of early childhood education with interactive mathlearning at home systems, empowered by recent advances in conversational AItechnologies, is slowly becoming a reality. With this motivation, we implementa multimodal dialogue system to support play-based learning experiences athome, guiding kids to master basic math concepts. This work explores SpokenLanguage Understanding (SLU) pipeline within a task-oriented dialogue systemdeveloped for Kid Space, with cascading Automatic Speech Recognition (ASR) andNatural Language Understanding (NLU) components evaluated on our homedeployment data with kids going through gamified math learning activities. Wevalidate the advantages of a multi-task architecture for NLU and experimentwith a diverse set of pretrained language representations for IntentRecognition and Entity Extraction tasks in the math learning domain. Torecognize kids' speech in realistic home environments, we investigate severalASR systems, including the commercial Google Cloud and the latest open-sourceWhisper solutions with varying model sizes. We evaluate the SLU pipeline bytesting our best-performing NLU models on noisy ASR output to inspect thechallenges of understanding children for math learning in authentic homes.

Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home