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Quantifying the value of pronunciation lexicons for keyword search in low resource languages
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In: http://www.clsp.jhu.edu/%7Eguoguo/papers/icassp2013_lexicon_value.pdf (2013)
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Sequential system combination for machine translation of speech
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In: http://www.clsp.jhu.edu/~damianos/slt08_mt_combination.pdf (2008)
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Unsupervised Learning of Acoustic Subword Units
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In: http://www.clsp.jhu.edu/~balakris/pubs/acl08superbrief.pdf (2008)
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Abstract:
Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a hidden Markov model (HMM); states and short state-sequences through this HMM correspond to the learnt sub-word units. The algorithm, originally proposed for unsupervised learning of allophonic variations within a given phoneme set, has been adapted to learn without any knowledge of the phonemes. An evaluation methodology is also proposed, whereby the state-sequence that aligns to a test utterance is transduced in an automatic manner to a phoneme-sequence and compared to its manual transcription. Over 85 % phoneme recognition accuracy is demonstrated for speaker-dependent learning from fluent, large-vocabulary speech. 1 Automatic Discovery of Phone(me)s Statistical models learnt from data are extensively used in modern automatic speech recognition (ASR) systems. Transcribed speech is used to estimate conditional models of the acoustics given a phonemesequence. The phonemic pronunciation of words and the phonemes of the language, however, are derived almost entirely from linguistic knowledge. In this paper, we investigate whether the phonemes may be learnt automatically from the speech signal. Automatic learning of phoneme-like units has significant implications for theories of language acquisition in babies, but our considerations here are somewhat more technological. We are interested in developing ASR systems for languages or dialects
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URL: http://www.clsp.jhu.edu/~balakris/pubs/acl08superbrief.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.219.4248
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| 4 |
Unsupervised learning of acoustic sub-word units
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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
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In: http://www.clsp.jhu.edu/~woosung/pdf/talip04.pdf (2004)
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Contemporaneous Text as Side-Information in Statistical Language Modeling
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In: http://www.clsp.jhu.edu/~woosung/pdf/csl04.pdf (2004)
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| 7 |
Cross-lingual latent semantic analysis for LM
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In: http://www.clsp.jhu.edu/~woosung/pdf/icassp04.pdf (2004)
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| 8 |
Language Model Adaptation Using Cross-Lingual Information
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In: http://www.clsp.jhu.edu/~woosung/pdf/euro03.pdf (2003)
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Cross-Lingual Lexical Triggers in Statistical Language Modeling
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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
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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
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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
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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
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In: http://www.cs.jhu.edu/~junwu/gbo.ps (2002)
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| 14 |
Using Cross-Language Cues For Story-Specific Language Modeling
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In: http://www.clsp.jhu.edu/~woosung/pdf/icslp02.pdf (2002)
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| 15 |
Smoothing Issues in the Structured Language Model
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In: http://cs.jhu.edu/~junwu/eurospeech01.ps (2001)
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Smoothing Issues in the Structured Language Mode
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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
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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
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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
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In: http://www.se.cuhk.edu.hk/PEOPLE/hmmeng/MEI_Final_Report2000.pdf (2000)
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Pronunciation modeling by sharing Gaussian densities across phonetic models
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In: http://mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro99.pdf (2000)
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