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1
Quantifying the value of pronunciation lexicons for keyword search in low resource languages
In: http://www.clsp.jhu.edu/%7Eguoguo/papers/icassp2013_lexicon_value.pdf (2013)
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2
Sequential system combination for machine translation of speech
In: http://www.clsp.jhu.edu/~damianos/slt08_mt_combination.pdf (2008)
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3
Unsupervised Learning of Acoustic Subword Units
In: http://www.clsp.jhu.edu/~balakris/pubs/acl08superbrief.pdf (2008)
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4
Unsupervised learning of acoustic sub-word units
In: http://aclweb.org/anthology-new/P/P08/P08-2042.pdf (2008)
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5
Lexical triggers and latent semantic analysis for crosslingual language model adaptation
In: http://www.clsp.jhu.edu/~woosung/pdf/talip04.pdf (2004)
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6
Contemporaneous Text as Side-Information in Statistical Language Modeling
In: http://www.clsp.jhu.edu/~woosung/pdf/csl04.pdf (2004)
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7
Cross-lingual latent semantic analysis for LM
In: http://www.clsp.jhu.edu/~woosung/pdf/icassp04.pdf (2004)
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8
Language Model Adaptation Using Cross-Lingual Information
In: http://www.clsp.jhu.edu/~woosung/pdf/euro03.pdf (2003)
Abstract: The success of statistical language modeling techniques is crucially dependent on the availability of a large amount training text. For a language in which such large text collections are not available, methods have recently been proposed to take advantage of a resource-rich language, together with cross-lingual information retrieval and machine translation, to sharpen language models for the resource-deficient language. In this paper, we describe investigations into such language models for an automatic speech recognition system for Mandarin Broadcast News. By exploiting a large side-corpus of contemporaneous English news articles to adapt a static Chinese language model to the news story being transcribed, we demonstrate significant improvements in recognition accuracy. The improvement from using English text is greater when less Chinese text is available to estimate the static language model. We also compare our cross-lingual adaptation to monolingual topic-dependent language model adaptation, and achieve further gains by combining the two adaptation techniques.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.2954
http://www.clsp.jhu.edu/~woosung/pdf/euro03.pdf
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9
Cross-Lingual Lexical Triggers in Statistical Language Modeling
In: http://acl.ldc.upenn.edu/W/W03/W03-1003.pdf (2003)
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10
Cross-Lingual Lexical Triggers in Statistical Language Modeling
In: http://www.clsp.jhu.edu/~woosung/pdf/emnlp03.pdf (2003)
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11
Contemporaneous Text as Side-Information in Statistical Language Modeling
In: http://www.clsp.jhu.edu/~sanjeev/Pubs/CSL2003b.pdf (2003)
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12
Making miracles: Interactive translingual search for cebuano and hindi
In: http://www.sis.pitt.edu/~daqing/docs/talip-final.pdf (2003)
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13
Maximum Entropy Language Modeling with Non-Local and Syntactic Dependencies
In: http://www.cs.jhu.edu/~junwu/gbo.ps (2002)
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14
Using Cross-Language Cues For Story-Specific Language Modeling
In: http://www.clsp.jhu.edu/~woosung/pdf/icslp02.pdf (2002)
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15
Smoothing Issues in the Structured Language Model
In: http://cs.jhu.edu/~junwu/eurospeech01.ps (2001)
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16
Smoothing Issues in the Structured Language Mode
In: http://www.clsp.jhu.edu/~woosung/pdf/euro01.pdf (2001)
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17
Pronunciation modeling by sharing Gaussian densities across phonetic models,” Computer Speech and Language
In: http://busim.ee.boun.edu.tr/~speech/publications/Speech_Recognition/eurospeech99.pdf (2000)
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18
Pronunciation modeling by sharing Gaussian densities across phonetic models
In: ftp://svr-ftp.eng.cam.ac.uk/pub/reports/nock_csl00.pdf (2000)
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19
Mandarin-English Information (MEI): Investigating Translingual Speech Retrieval
In: http://www.se.cuhk.edu.hk/PEOPLE/hmmeng/MEI_Final_Report2000.pdf (2000)
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20
Pronunciation modeling by sharing Gaussian densities across phonetic models
In: http://mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro99.pdf (2000)
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