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