Multi-view learning with supervision for transformed bottleneck features
Raman Arora, Karen Livescu · 2014
Previous work has shown that acoustic features can be improved by unsupervised learning of transformations based on canonical correlation analysis (CCA) using articulatory measurements that are available at training time. In this paper, we investigate whether this second view (articulatory data) still helps even when labels are also available at training time. We begin with strong baseline bottleneck features, which can be learned when the training set is phonetically labeled. We then compare several options for learning transformations of the bottleneck features in the presence of both articulatory measurements and phonetic labels for the training data. The methods compared include combinations of LDA and CCA, as well as a three-view extension of CCA that simultaneously uses the labels and articulatory measurements as additional views. Phonetic recognition experiments on data from the University of Wisconsin X-ray microbeam database show that the learned features improve performance over using either just the labels or just the articulatory measurements for learning acoustic transformations.