We present a maximally streamlined approach to learning HMM-based acoustic models for automatic speech recognition. In our approach, an initial monophone HMM is iteratively refin...
We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards ...
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, ea...
We propose a new unsupervised learning method to obtain a layered pictorial structure (LPS) representation of an articulated object from video sequences. It will be seen that this...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
We present a formal description of a neurofuzzy system capable of aligning two sequences recognizing their internal structure. The alignment is done on two levels: grouping of the...