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Research area
Natural language & AI

Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on


Nikil Jayant, Mat Hans, Tajana Simunic, Andrea Acquaviva

A low-power, fixed-point, front-end feature extraction for a distributed speech recognition system


This work describes the optimization of a signal processing front-end for a distributed speech recognition system with the goal of reducing power consumption. Two categories of source code opti­mizations were used, architectural and algorithmic. Architectural optimizations reduce the power consumption for a particular sys­tem, in this case, the HP Labs Smartbadge IV prototype portable system. Algorithmic optimizations are more general and involve changes in the algorithmic implementation of the source code to run faster and consume less power. A cycle accurate energy simu­lation shows a reduction in power usage by 83.5% with these opti­mizations. The optimized source code runs 34 times faster than the original code, therefore it can run at lower processor clock speeds and voltages for further reductions in power consumption. This technique, known as dynamic voltage scaling, was implemented on the Smartbadge IV hardware for an overall reduction in power usage of 89.2%.

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