4 accepted papers
This paper explores the use of multi-view features and their discriminative transforms in a convolutional deep neural network (CNN) architecture for a continuous large vocabulary speech recognition task. Mel-filterbank energies and perceptually motivated forced damped oscillator coefficient (DOC) fe…
Unseen data can degrade performance of deep neural net (DNN) acoustic models. To cope with unseen data, adaptation techniques are deployed. For unlabeled unseen data, one must generate some hypothesis given an existing model, which is used as the label for model adaptation. However, assessing the go…
Articulatory information can effectively model variability in speech and can improve speech recognition performance under varying acoustic conditions. Learning speaker-independent articulatory models has always been challenging, as speaker-specific information in the articulatory and acoustic spaces…
Speech recognition in varying background conditions is a challenging problem. Acoustic condition mismatch between training and evaluation data can significantly reduce recognition performance. For mismatched conditions, data-adaptation techniques are typically found to be useful, as they expose the…