Design and optimization of a low resource speech recognition system
ZHANG Pengyuan1, JI Zhe2, HOU Wei2, JIN Xin2, HAN Weisheng1
1. Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;
2. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
Abstract：Wearable devices and smart home systems need speech recognition engines with few resources and high rejection rates. Traditional methods cannot provide such systems. This paper presents algorithms for decoding and rejection for a low source speech recognition system. The decoding improves the rejection rate up to 64.8% by changing the filler reentry while the memory is only increased 8.5 kB compared with the baseline system. The rejection algorithm computes a background probability which is compared to similar probabilities calculated in advance online decoding. The system gives a rejection rate of 93.8% with little loss in the recognition rate. The memory and computational speed are also optimized.
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ZHANG Pengyuan, JI Zhe, HOU Wei, JIN Xin, HAN Weisheng. Design and optimization of a low resource speech recognition system. Journal of Tsinghua University(Science and Technology), 2017, 57(2): 147-152.
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2017, Vol. 57 
