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Title:Accelerating lattice scoring of automatic speech recognition through acoustic pre-pruning on GPU
Author(s):He, Di
Advisor(s):Chen, Deming
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):acoustic pre-pruning
lattice scoring
Gaussian mixture model (GMM)
graphic processing unit (GPU)
Abstract:This thesis introduces an acoustic pre-pruning algorithm that speeds up lattice scoring for GMM based ASR systems, and a constrained agglomerative clustering algorithm that makes it possible to maintain the advantage of the new algorithm in a GPU implementation. The implementation undergoes 2% to 6% degradation in PER while accelerating the runtime of lattice scoring by 45X to 60X over a traditional CPU implementation.
Issue Date:2015-01-21
Rights Information:Copyright 2014 Di He
Date Available in IDEALS:2015-01-21
Date Deposited:2014-12

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