Multiple sequence alignment based bootstrapping for improved incremental word learning

We investigate incremental word learning with few training examples in a Hidden Markov Model (HMM) framework suitable for an interactive learning scenario with little prior knowledge. When using only a few training examples the initialization of the models is a crucial step. In the bootstrapping approach proposed, an unsupervised initialization of the parameters is performed, followed by the retraining and construction of a new HMM using multiple sequence alignment (MSA). Finally we analyze discriminative training techniques to increase the separability of the classes using minimum classification error (MCE). Recognition results are reported on isolated digits taken from the TIDIGITS database.