WEakly supervised hmm learning for spoken word acquisition in human computer interaction with little manual effort KU Leuven
© 2014 IEEE. In this paper, weakly supervised HMM learning is applied to modeling word acquisition towards human-computer interaction with little manual effort. The only imposed supervisory information is initializing the learning algorithms by two labeled data samples per pattern. Experiments on TIDIG-ITS show that our recently proposed algorithm, Baum-Welch learning regularized by non-negative Tucker decomposition, succeeds in finding good ...