Graph-Based Learning for Speech Processing


Current methods for acoustic modeling in automatic speech processing focus train complex classifiers from a training set, followed by adaptation to the test data. Semi-supervised learning methods, on the other hand, take unlabeled data into account during the initial training process. Thus, the classifier is guided to take into account the underlying global properties of the test data. In this project we investigate semi-supervised graph-based learning algorithms and their application to acoustic modeling in speech processing. Our primary interest is in developing high-performing, scalable classification methods that are suited to the stochastic nature of speech signals and are applicable to large data sets.

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