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Efficient embedded speech recognition for very large vocabulary Mandarin car-navigation systems

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3 Author(s)
Yanmin Qian ; Dept. of Electron. Eng., Tsinghua Univ., Beijing, China ; Jia Liu ; Johnson, M.T.

Automatic speech recognition (ASR) for a very large vocabulary of isolated words is a difficult task on a resource-limited embedded device. This paper presents a novel fast decoding algorithm for a Mandarin speech recognition system which can simultaneously process hundreds of thousands of items and maintain high recognition accuracy. The proposed algorithm constructs a semi-tree search network based on Mandarin pronunciation rules, to avoid duplicate syllable matching and save redundant memory. Based on a two-stage fixed-width beam-search baseline system, the algorithm employs a variable beam-width pruning strategy and a frame-synchronous word-level pruning strategy to significantly reduce recognition time. This algorithm is aimed at an in-car navigation system in China and simulated on a standard PC workstation. The experimental results show that the proposed method reduces recognition time by nearly 6-fold and memory size nearly 2- fold compared to the baseline system, and causes less than 1% accuracy degradation for a 200,000 word recognition task.

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:55 ,  Issue: 3 )

Date of Publication:

August 2009

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