NMF-based Keyword Learning from Scarce Data.

This research is situated in a project aimed at the development of a vocal user interface (VUI) that learns to understand its users specifically persons with a speech impairment. The vocal interface adapts to the speech of the user by learning the vocabulary from interaction examples. Word learning is implemented through weakly supervised non-negative matrix factorization (NMF). The goal of this study is to investigate how we can improve word learning when the number of interaction examples is low. We demonstrate two approaches to train NMF models on scarce data: 1) training word models using smoothed training data, and 2) training word models that strictly correspond to the grounding information derived from a few interaction examples. We found that both approaches can substantially improve word learning from scarce training data.