WEakly supervised hmm learning for spokenword acquisition in human computer interaction with little manual effort

摘要

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 solutions in the sense of yielding high recognition accuracy on the testing data which approximate the supervised baseline (98.0% vs 98.9%).