Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home
This work addresses the challenge of building robust conversational AI for early childhood education at home, but it is incremental as it focuses on evaluating existing methods in a specific domain.
The authors tackled the problem of understanding children's speech in home environments for interactive math learning by evaluating a Spoken Language Understanding pipeline with ASR and NLU components, finding that multi-task NLU architectures and pretrained models improved performance on noisy ASR output.
Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality. With this motivation, we implement a multimodal dialogue system to support play-based learning experiences at home, guiding kids to master basic math concepts. This work explores Spoken Language Understanding (SLU) pipeline within a task-oriented dialogue system developed for Kid Space, with cascading Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) components evaluated on our home deployment data with kids going through gamified math learning activities. We validate the advantages of a multi-task architecture for NLU and experiment with a diverse set of pretrained language representations for Intent Recognition and Entity Extraction tasks in the math learning domain. To recognize kids' speech in realistic home environments, we investigate several ASR systems, including the commercial Google Cloud and the latest open-source Whisper solutions with varying model sizes. We evaluate the SLU pipeline by testing our best-performing NLU models on noisy ASR output to inspect the challenges of understanding children for math learning in authentic homes.