Abstract
As the automatic speech recognition technology (ASR) has becoming more and more mature, especially with statistical language modeling built with web scale data, and with the utilization of Hidden Markov Model probabilistic framework, speech recognition has become applicable to many domains and usage scenarios. In particular, speech recognition can be applied to task such as Chinese postal address recognition. This paper presents the first attempt ever, in both academic and commercial settings, to create an ASR-based input method for postal address recognition in Chinese Mandarin. By customizing the statistical language model to such domain, and incorporating the knowledge from the structural information provided by geo-topology, our language model successfully captures the signals from geographical contextual information and self-correct possible mis-recognitions. Experiment results provide evident that our approach based on speech recognition achieves a faster and a more accuracy input method compare to traditional keyboard-based input.
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Wei, L.F., Maosong, S. (2014). ASR-Based Input Method for Postal Address Recognition in Chinese Mandarin. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_27
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DOI: https://doi.org/10.1007/978-3-319-12277-9_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12276-2
Online ISBN: 978-3-319-12277-9
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