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The analysis and design of architecture systems for speech recognition on modern handheld-computing devices
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Source International Symposium on Systems Synthesis archive
Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis table of contents
Newport Beach, CA, USA
SESSION: Case studies in embedded systems table of contents
Pages: 65 - 70  
Year of Publication: 2003
ISBN:1-58113-742-7
Authors
Andreas Hagen  University of Colorado at Boulder, Boulder, CO
Daniel A. Connors  University of Colorado at Boulder, Boulder, CO
Bryan L. Pellom  University of Colorado at Boulder, Boulder, CO
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Growing demand for high performance in embedded systems is creating new opportunities to use speech recognition systems traditionally executed only on high performance systems. In several ways, the needs of embedded computing differ from those of more traditional general-purpose systems. Embedded systems have more stringent constraints on cost and power consumption that lead to design bottlenecks for many computationally-intensive applications. This paper characterizes the speech recognition process on hand-held mobile devices and evaluates the use of modern architecture features and compiler techniques for performing real-time speech recognition. We evaluate the University of Colorado Sonic speech recognition software on the IMPACT architectural simulator and compiler framework. Experimental results show that by using a strategic set of compiler optimization, a 500MHz processor with moderate levels of instruction-level parallelism and cache resources can meet the real-time computing and power constraints of an advanced speech recognition application.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Andreas Hagen: colleagues
Daniel A. Connors: colleagues
Bryan L. Pellom: colleagues

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