We investigate incremental word learning in a Hidden Markov Model (HMM) framework suitable for human-robot interaction. In interactive learning, the tutoring time is a crucial factor. Hence our goal is to use as few training samples as possible while maintaining a good performance level. To adapt the states of the HMMs, different large-margin discriminative training strategies for increasing the separability of the classes are proposed. We also present a novel estimation of the variance floor when a very low number of training data is used. Finally our approach is successfully evaluated on isolated digits taken from the TIDIGITS database.
Bibliographic reference. Ayllón Clemente, Irene / Heckmann, Martin / Denecke, Alexander / Wrede, Britta / Goerick, Christian (2010): "Incremental word learning using large-margin discriminative training and variance floor estimation", In INTERSPEECH-2010, 889-892.