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Your search keyword '"Ryu Pum Mo"' showing total 27 results
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27 results on '"Ryu Pum Mo"'

2. An Empirical Research on Extracting Relations from Wikipedia Text

Author

Huang, Jin-Xia, Ryu, Pum-Mo, Choi, Key-Sun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fyfe, Colin, editor, Kim, Dongsup, editor, Lee, Soo-Young, editor, and Yin, Hujun, editor

Published
2008
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3. An Effective Document Classification System Based on Concept Probability Vector

Author

Kang, Hyun-Kyu, Hwang, Yi-Gyu, Ryu, Pum-Mo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chi, Chi-Hung, editor, and Lam, Kwok-Yan, editor

Published
2004
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27. Improving code-switched ASR with linguistic information

Author

Chi, Jie, Bell, Peter, Calzolari, Nicoletta, Huang, Chu-Ren, Kim, Hansaem, Pustejovsky, James, Wanner, Leo, Choi, Key-Sun, Ryu, Pum-Mo, Chen, Hsin-Hsi, Donatelli, Lucia, Ji, Heng, Kurohashi, Sadao, Paggio, Patrizia, Xue, Nianwen, Kim, Seokhwan, Hahm, Younggyun, He, Zhong, Lee, Tony Kyungil, Santus, Enrico, Bond, Francis, and Na, Seung-Hoon

Abstract

This paper seeks to improve the performance of automatic speech recognition (ASR) systems operating on code-switched speech. Code-switching refers to the alternation of languages within a conversation, a phenomenon that is of increasing importance considering the rapid rise in the number of bilingual speakers in the world. It is particularly challenging for ASR owing to the relative scarcity of code-switching speech and text data, even when the individual languages are themselves well-resourced. This paper proposes to overcome this challenge by applying linguistic theories in order to generate more realistic code-switching text, necessary for language modelling in ASR. Working with English-Spanish code-switching, we find that Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation, and bring 2% improvement to ASR performance.

Published
2022

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