Incremental word learning using large-margin discriminative training and variance floor estimation

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.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Irene Ayllón Clemente, Martin Heckmann, Alexander Denecke, Britta Wrede, Christian Goerick
• Keywords: Discriminative Training Strategies, Hidden Markov Model, INTERSPEECH 2010, Signal Processing, Training