Design and Improvement of Sequence-level Objective Functions for DNN-based Large Vocabulary Continuous Speech Recognition, Ph.D. Dissertation Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
This thesis focuses on the problem of large vocabulary continuous speech recognition (LVCSR).Numerous research results in recent years proved effectiveness of deep neural networks (DNN) for LVCSR. As a result, many methods were proposed to incorporate DNNs in LVCSR. From one perspective we can look at these methods from the viewpoint of objective functions used for training DNNs. A frame-level objective function is one that is defined on frames locally, whereas a sequence-level objective function is defined on whole sequences. Since speech recognition is essentially a sequentional problem, here we focus on designing and imroving sequencelevel objective functions for DNNs. The main proposed...
Cataloging briefDesign and Improvement of Sequence-level Objective Functions for DNN-based Large Vocabulary Continuous Speech Recognition, Ph.D. Dissertation Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
This thesis focuses on the problem of large vocabulary continuous speech recognition (LVCSR).Numerous research results in recent years proved effectiveness of deep neural networks (DNN) for LVCSR. As a result, many methods were proposed to incorporate DNNs in LVCSR. From one perspective we can look at these methods from the viewpoint of objective functions used for training DNNs. A frame-level objective function is one that is defined on frames locally, whereas a sequence-level objective function is defined on whole sequences. Since speech recognition is essentially a sequentional problem, here we focus on designing and imroving sequencelevel objective functions for DNNs. The main proposed...
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