Accelerating lattice scoring of automatic speech recognition through acoustic pre-pruning on GPU
He, Di
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https://hdl.handle.net/2142/73019
Description
Title
Accelerating lattice scoring of automatic speech recognition through acoustic pre-pruning on GPU
Author(s)
He, Di
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Chen, Deming
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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