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Performance analysis and optimization of automatic speech recognition

Author
Tabani, H.; Arnau, J.; Tubella, J.; Gonzalez, A.
Type of activity
Journal article
Journal
IEEE Transactions on Multi-Scale Computing Systems
Date of publication
2018-10-01
Volume
4
Number
4
First page
847
Last page
860
DOI
https://doi.org/10.1109/TMSCS.2017.2739158 Open in new window
Project funding
Intelligent, Ubiquitous and Energy-Efficient Computing Systems
Microarchitecture and Compilers for Future Processors III
Repository
http://hdl.handle.net/2117/128336 Open in new window
URL
https://ieeexplore.ieee.org/document/8010340 Open in new window
Abstract
Fast and accurate Automatic Speech Recognition (ASR) is emerging as a key application for mobile devices. Delivering ASR on such devices is challenging due to the compute-intensive nature of the problem and the power constraints of embedded systems. In this paper, we provide a performance and energy characterization of Pocketsphinx, a popular toolset for ASR that targets mobile devices. We identify the computation of the Gaussian Mixture Model (GMM) as the main bottleneck, consuming more than 80...
Citation
Tabani, H. [et al.]. Performance analysis and optimization of automatic speech recognition. "IEEE Transactions on Multi-Scale Computing Systems", 1 Octubre 2018, vol. 4, núm. 4, p. 847-860.
Keywords
Automatic speech recognition, Gaussian mixture models, Vectorization
Group of research
ARCO - Microarchitecture and Compilers

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