Weakly-supervised word learning is improved by an active online algorithm

When infants learn words, they do not generally occur in isolation but as parts of continuous utterances and with several possible related meanings. Details of the efficient learning techniques of infants remain unknown. In this paper we introduce a dynamic concept matrix (DCM) algorithm that learns acoustic models for a set of keywords from given pairs of continuous utterances and related keyword labels. DCM is an incremental, active online algorithm. Specifically, each training utterance is first recognized with the current word models, and the recognition result is used to guide training further. In lownoise conditions DCM shows significant improvement in convergence rate and final recognition scores to an earlier passive CM model on TIDIGITS and CAREGIVER UK Y2 datasets. The results suggest that in ambiguous learning situations it may be beneficial for the learner to observe the learning situation, make hard decisions if some known words/objects were recognized and update the models based on the decisions.