11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Incremental Word Learning Using Large-Margin Discriminative Training and Variance Floor Estimation

Irene Ayllón Clemente (1), Martin Heckmann (2), Alexander Denecke (1), Britta Wrede (1), Christian Goerick (2)

(1) Universität Bielefeld, Germany
(2) Honda Research Institute Europe GmbH, Germany

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.

Full Paper

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.