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  • In this paper we present a highly optimized implementation of Gaussian mixture acoustic model evaluation algorithm. Evaluation of these likelihoods is one of the~most computationally intensive parts of automatics speech recognizers but it can be well-parallelized and offloaded to GPU devices. Our approach offers significant speed-up compared to the recently published approaches, since it exploits the GPU architecture better. All the recent implementations were programmed either in CUDA or OpenCL GPU programming frameworks. We present results for both; CUDA as well as OpenCL. Results suggest that even very large acoustic models can be utilized in real-time speech recognition engines on computers and laptops equipped with a low-end GPU. Optimization of acoustic likelihoods computation on GPU enables to use the remaining GPU resources for offloading of other compute-intensive parts of LVCSR decoder.
  • In this paper we present a highly optimized implementation of Gaussian mixture acoustic model evaluation algorithm. Evaluation of these likelihoods is one of the~most computationally intensive parts of automatics speech recognizers but it can be well-parallelized and offloaded to GPU devices. Our approach offers significant speed-up compared to the recently published approaches, since it exploits the GPU architecture better. All the recent implementations were programmed either in CUDA or OpenCL GPU programming frameworks. We present results for both; CUDA as well as OpenCL. Results suggest that even very large acoustic models can be utilized in real-time speech recognition engines on computers and laptops equipped with a low-end GPU. Optimization of acoustic likelihoods computation on GPU enables to use the remaining GPU resources for offloading of other compute-intensive parts of LVCSR decoder. (en)
Title
  • Optimization of the Gaussian Mixture Model Evaluation on GPU
  • Optimization of the Gaussian Mixture Model Evaluation on GPU (en)
skos:prefLabel
  • Optimization of the Gaussian Mixture Model Evaluation on GPU
  • Optimization of the Gaussian Mixture Model Evaluation on GPU (en)
skos:notation
  • RIV/49777513:23520/11:43898500!RIV12-TA0-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(2C06020), P(TA01011264), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 218772
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/11:43898500
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • GPU, CUDA, OpenCL, Gaussian Mixture Likelihood, speech recognition, asouctic modeling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3D262AAD4D4E]
http://linked.open...v/mistoKonaniAkce
  • Florencie, Itálie
http://linked.open...i/riv/mistoVydani
  • Red Hook, NY 12571, USA
http://linked.open...i/riv/nazevZdroje
  • 12th Annual Conference of the International Speech Communication Association 2011 (INTERSPEECH 2011)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Psutka, Josef
  • Vaněk, Jan
  • Trmal, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1990-9772
number of pages
http://purl.org/ne...btex#hasPublisher
  • Curran Associates, Inc.
https://schema.org/isbn
  • 978-1-61839-270-1
http://localhost/t...ganizacniJednotka
  • 23520
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