DocumentCode :
3513278
Title :
Low power embedded speech recognition system based on a MCU and a coprocessor
Author :
Peng Li ; Tang, Hua ; Liang, Weiqian
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Duluth, MN
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
625
Lastpage :
628
Abstract :
In speech recognition systems, CHMM (Continuous Hidden Markov Model) based speech recognition algorithms have the best accuracy but with the most computational cost. Neither General Purpose Processor (GPP) nor dedicated hardware implementation is a good solution for the algorithm, due to high power consumption for the former and lack of flexibility for the later. To reduce power consumption and enhance flexibility, this paper presents a speech recognition system composed of a coprocessor and a MCU. The coprocessor is a dedicated hardware design for Output Probability Calculation (OPC), which is the most computation-intensive part in CHMM, and MCU is a 32 bit RISC (ARM). Tested with a 358-state 3-mixture 27-feature 800-word HMM, MCU operates at 40 MHz and coprocessor operates at 10 MHz to meet real-time requirement. The power consumption of MCU is 10 mW, and coprocessor 1.8 mW.
Keywords :
coprocessors; hidden Markov models; speech recognition; continuous hidden Markov model; coprocessor; dedicated hardware design; general purpose processor; output probability calculation; power consumption; speech recognition system; Algorithm design and analysis; Coprocessors; Embedded computing; Energy consumption; Field programmable gate arrays; Hardware; Hidden Markov models; Power engineering and energy; Speech recognition; Table lookup; Coprocessors; FPGA; HMM; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4959661
Filename :
4959661
Link To Document :
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