Dissertation graph learning semi supervised learning

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Dissertation graph learning semi supervised learning Acoustic models require a large amount of training data. However, lots of labor is required to annotate the training data for automatic speech recognition.

dissertation graph learning semi supervised learning More importantly, the performance of the acoustic model could degenerate during test time, where the conditions of test data differ from the training data in speaker characteristics, channel and recording environment.

To compensate for the deviation between training and test conditions, we investigate a graph-based semi-supervised learning approach to acoustic modeling in automatic speech recognition.

Graph-based semi-supervised learning SSL is a widely used semi-supervised learning method in which the labeled data and unlabeled data are dissertation graph represented as a weighted graph, and the information is propagated from the learning semi data to the unlabeled data. The key assumption that graph-based SSL makes is that data dissertation graph lie on a low dimensional manifold, where samples that are close to each other are expected to learning semi supervised the same class label.

More importantly, by exploiting dissertation learning learning semi supervised learning relationship between training and test samples, graph-based SSL implicitly adapts to the test data. In this thesis, we address several key challenges in applying graph-based SSL to acoustic modeling. We supervised learning investigate and compare several state-of-the-art dissertation graph learning semi supervised learning SSL algorithms on a benchmark dataset.

Dissertation graph learning semi supervised learning addition, we propose novel graph construction methods that allow /i-need-to-write-an-essay-in-one-night-a-2000-word.html Dissertation graph learning semi supervised learning to handle variable-length input features. We compare two different integration frameworks for graph-based learning. First, we propose a dissertation graph learning semi supervised learning late integration chemistry help that combines graph-based SSL with the DNN-based acoustic modeling and evaluate the framework read more continuous word recognition tasks.

Second, we propose an early integration framework using neural graph embeddings and compare two different neural graph embedding features that capture the information of the manifold at different levels. The embedding features are used as input to a DNN system and are shown to outperform the conventional acoustic feature inputs on several medium-to-large vocabulary conversational speech recognition tasks.

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