An Automatic Speech Recognition Application Framework for Highly Parallel Implementations on the GPU

Jike Chong, Ekaterina Gonina, Dorothea Kolossa, Steffen Zeiler and Kurt Keutzer

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2012-47
April 26, 2012

http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-47.pdf

Data layout, data placement, and synchronization processes are not usually part of a speech application expert's daily concerns. Yet failure to carefully take these concerns into account in a highly parallel implementation on the graphics processing units (GPUs) could mean an order of magnitude of loss in application performance. In this paper we present an application framework for parallel programming of automatic speech recognition (ASR) applications that allows a speech application expert to effectively implement speech applications on the GPU. It is an approach for crystallizing and transferring the often tacit knowledge of effective parallel programming techniques while allowing for flexible adaptation to various application usage scenarios.

The application framework for parallel programming includes an application context description, a software architecture, a reference implementation, and a set of extension points for flexible customization. We describe how a speech expert can use the application framework in a parallel application design flow as well as present two case studies that illustrate the flexibility of the framework to adapt to different usage scenarios. The case studies show two examples in extending the framework to an advanced audio-only speech recognition application and an audio-visual recognition application that enables lip-reading in high noise recognition environments. The adaptation to the latter scenario also demonstrates how the ASR application framework has enabled a Matlab/Java programmer to effectively utilize a GPU to produce an implementation that achieves a 20x speedup in recognition throughput as compared to a sequential CPU implementation.


BibTeX citation:

@techreport{Chong:EECS-2012-47,
    Author = {Chong, Jike and Gonina, Ekaterina and Kolossa, Dorothea and Zeiler, Steffen and Keutzer, Kurt},
    Title = {An Automatic Speech Recognition Application Framework for Highly Parallel Implementations on the GPU},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2012},
    Month = {Apr},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-47.html},
    Number = {UCB/EECS-2012-47},
    Abstract = {  Data layout, data placement, and synchronization processes are not
  usually part of a speech application expert's daily concerns. Yet
  failure to carefully take these concerns into account in a highly
  parallel implementation on the graphics processing units (GPUs) could
  mean an order of magnitude of loss in application performance. In
  this paper we present an application framework for parallel
  programming of automatic speech recognition (ASR) applications that
  allows a speech application expert to effectively implement speech
  applications on the GPU. It is an approach for
  crystallizing and transferring the often tacit knowledge of effective
  parallel programming techniques while allowing for flexible
  adaptation to various application usage scenarios.

  The application framework for parallel programming includes an application context description, a software
    architecture, a reference implementation, and a set of extension points for flexible customization. We describe how
  a speech expert can use the application framework in a parallel
  application design flow as well as present two case studies that
  illustrate the flexibility of the framework to adapt to different
  usage scenarios. The case studies show two examples in extending the
  framework to an advanced audio-only speech recognition application
  and an audio-visual recognition application that enables lip-reading
  in high noise recognition environments. The adaptation to the latter
  scenario also demonstrates how the ASR application framework has
  enabled a Matlab/Java programmer to effectively utilize a GPU to
  produce an implementation that achieves a 20x speedup in recognition
  throughput as compared to a sequential CPU implementation.}
}

EndNote citation:

%0 Report
%A Chong, Jike
%A Gonina, Ekaterina
%A Kolossa, Dorothea
%A Zeiler, Steffen
%A Keutzer, Kurt
%T An Automatic Speech Recognition Application Framework for Highly Parallel Implementations on the GPU
%I EECS Department, University of California, Berkeley
%D 2012
%8 April 26
%@ UCB/EECS-2012-47
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-47.html
%F Chong:EECS-2012-47