2026-09-18T21:56:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/792172022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
James R. Glass.
Zhang, Yaodong, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-06-17T19:48:20Z
2013-06-17T19:48:20Z
2013
2013
http://hdl.handle.net/1721.1/79217
844753073
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 163-173).
This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms greatly outperform the conventional DTW in a single-threaded computing environment. Third, we describe the parallel implementation of the lower-bounded DTW search algorithm. Experimental results indicate that the total running time of the entire spoken detection system grows linearly with corpus size. We also present the training of large Deep Belief Networks (DBNs) on Graphical Processing Units (GPUs). The phonetic classification experiment on the TIMIT corpus showed a speed-up of 36x for pre-training and 45x for back-propagation for a two-layer DBN trained on the GPU platform compared to the CPU platform.
by Yaodong Zhang.
Ph.D.
173 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Unsupervised speech processing with applications to query-by-example spoken term detection
Thesis
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<Abstract>This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms greatly outperform the conventional DTW in a single-threaded computing environment. Third, we describe the parallel implementation of the lower-bounded DTW search algorithm. Experimental results indicate that the total running time of the entire spoken detection system grows linearly with corpus size. We also present the training of large Deep Belief Networks (DBNs) on Graphical Processing Units (GPUs). The phonetic classification experiment on the TIMIT corpus showed a speed-up of 36x for pre-training and 45x for back-propagation for a two-layer DBN trained on the GPU platform compared to the CPU platform.</Abstract>
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