Acquisition of ordinal words using weakly supervised NMF

Abstract

This paper issues in the design of a vocal interface for a robot that can learn to understand spoken utterances through demonstration. Weakly supervised non-negative matrix factorization (NMF) is used as a machine learning algorithm where acoustic data are augmented with semantic labels representing the meaning of the command. Many parameters that the robot needs in order to execute the commands have an ordinal structure. Constrained subspace NMF (CSNMF) is proposed as an extension to NMF that aims to better deal with ordinal data and thus increase the learning rate of the grounding information with an ordinal structure. Furthermore automatic relevance determination is used to deal with model order selection. The use of CSNMF yields a significant improvement in the learning rate and accuracy when recognising ordinal parameters.