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

Yanmin Qian; Jia Liu; Michael T. Johnson

doi:10.1109/TCE.2009.5278018

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

Yanmin Qian
, Jia Liu
, Michael T. Johnson

Producción científica: Article › revisión exhaustiva

11 Citas (Scopus)

Resumen

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.

Idioma original	English
Páginas (desde-hasta)	1496-1500
Número de páginas	5
Publicación	IEEE Transactions on Consumer Electronics
Volumen	55
N.º	3
DOI	https://doi.org/10.1109/TCE.2009.5278018
Estado	Published - 2009

Nota bibliográfica

Funding Information:
1This work was supported by the National High Technology Research and Development Program of China (NO. 2006AA010101 and NO. 2007AA04Z223) and National Natural Science Foundation of China and Microsoft Research Asia (NO. 60776800). Yanmin Qian and Jia Liu are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]). Michael T. Johnson is with the Department of Electrical Engineering, Marquette University, Milwaukee, Wisconsin 53201, USA. He is now a visiting professor with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]). Contributed Paper Manuscript received June 11, 2009 0098 3063/09/$20.00 © 2009 IEEE

Financiación

1This work was supported by the National High Technology Research and Development Program of China (NO. 2006AA010101 and NO. 2007AA04Z223) and National Natural Science Foundation of China and Microsoft Research Asia (NO. 60776800). Yanmin Qian and Jia Liu are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]). Michael T. Johnson is with the Department of Electrical Engineering, Marquette University, Milwaukee, Wisconsin 53201, USA. He is now a visiting professor with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]). Contributed Paper Manuscript received June 11, 2009 0098 3063/09/$20.00 © 2009 IEEE

Financiadores	Número del financiador
Microsoft Research Asia	60776800
National High Technology Research and Development Program of China	2006AA010101, 2007AA04Z223
National Natural Science Foundation of China (NSFC)

ASJC Scopus subject areas

Media Technology
Electrical and Electronic Engineering

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10.1109/TCE.2009.5278018

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title = "Efficient embedded speech recognition for very large vocabulary Mandarin car-navigation systems",

abstract = "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.",

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N2 - 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.

AB - 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.

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

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