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Hits 1 – 20 of 21

1	General
	Studia Linguistica Upsaliensia; Edited Anju Saxena; Åke Viberg...
	In: http://uu.diva-portal.org/smash/get/diva2%3A275878/FULLTEXT03.pdf (2009)
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2	The Software Infrastructure For The Development And Validation Of The Greek Wordnet
	Maria Grigoriadou; Harry Kornilakis; Eleni Galiotou...
	In: http://cgi.di.uoa.gr/~harryk/papers/RJIST.pdf (2004)
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3	GlobalPhone: A Multilingual Speech and Text Database Developed at Karlsruhe University
	Tanja Schultz
	In: http://www-2.cs.cmu.edu/~tanja/Papers/schultz_icslp02.pdf (2002)
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4	Polyphone Decision Tree Specialization for Language Adaptation
	T. Schultz; A. Waibel
	In: http://www-2.cs.cmu.edu/~tanja/Papers/schultz_icassp00-multi.pdf (2000)
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5	Experiments towards a Multi-language LVCSR Interface
	Tanja Schultz; Alex Waibel
	In: http://www-2.cs.cmu.edu/~tanja/Papers/schultz_icmi99.ps.gz (2000)
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6	Experiments towards a multi-language LVCSR interface
	Tanja Schultz; Alex Waibel
	In: http://www.ri.cmu.edu/pub_files/pub1/schultz_tanja_1999_2/schultz_tanja_1999_2.pdf (2000)
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7	Polyphone Decision Tree Specialization For Language Adaptation
	T. Schultz; A. Waibel
	In: http://werner.ira.uka.de/~tanja/Papers/schultz_icassp00-multi.ps.gz (2000)
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8	The CHILDES System
	Brian MacWhinney; Brian Macwhinney
	In: http://www.ai.mit.edu/people/jimmylin/papers/MacWhinney96.pdf (1996)
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9	The limits to variation in Turkish nominal morphosyntax. In: Timothy J. Curnow (ed) Selected papers from the 2007 Conference of the Australian Linguistic Society. [Available at: http://www.als.asn.au
	Alan Reed Libert
	In: http://www.als.asn.au/proceedings/als2007/libert.pdf (1978)
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10	Siemens AG
	Ute Ziegenhain; Hanne Fersøe; Henk Van Den Heuvel...
	In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/358_paper.pdf
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11	QUICK FACTS FOR FRENCH TEACHERS Quick Responses to Common Misconceptions Compiled by
	Madeline Turan
	In: http://www.frenchteachers.org/QuickFactsforFrenchTeachers.pdf
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12	Siemens AG
	Ute Ziegenhain; Hanne Fersøe; Henk Van Den Heuvel...
	In: http://lands.let.kun.nl/literature/heuvel.2008.4.pdf
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13	The Full-to-Partial Reduction in Korean and Turkish Reduplication·
	Hyung-soo Kim
	In: http://roa.rutgers.edu/files/1074-0510/1074-KIM-0-0.PDF
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14	Overview of Stemming Algorithms for Indian and Non-Indian Languages
	Dalwadi Bijal; Suthar Sanket
	In: http://ijcsit.com/docs/Volume+5/vol5issue02/ijcsit2014050241.pdf
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15	More Students Attended FASL than CONSOLE*
	Roumyana Pancheva
	In: http://www-bcf.usc.edu/~pancheva/fasl2009_pancheva.pdf
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16	Time (s)
	Eduardo Reck Miranda
	In: http://cmr.soc.plymouth.ac.uk/publications/lmj_15.pdf
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17	Morph-Based Speech Recognition and Modeling of Out-of-Vocabulary Words Across Languages
	Mathias Creutz; Teemu Hirsimäki; Mikko Kurimo; Antti Puurula; Janne Pylkkönen; Vesa Siivola; Matti Varjokallio; Andreas Stolcke
	In: http://www-speech.sri.com/cgi-bin/run-distill?papers/acm2007-morph-asr.ps.gz
	Abstract: We explore the use of morph-based language models in large-vocabulary continuous speech recognition systems across four so-called “morphologically rich ” languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the Morfessor algorithm. By estimating n-gram language models over sequences of morphs instead of words, the quality of the language model is improved through better vocabulary coverage and reduced data sparsity. Standard word models suffer from high out-of-vocabulary (OOV) rates, whereas the morph models can recognize previously unseen word forms by concatenating morphs. It is shown that the morph models do perform fairly well on OOVs without compromising the recognition accuracy on in-vocabulary words. The Arabic experiment constitutes the only exception, since here the standard word model outperforms the morph model. Differences in the data sets and the amount of data are discussed as a plausible explanation.
	Keyword: Algorithms; and synthesis; Egyptian Colloquial Arabic; Estonian; Experimentation; Finnish; Highly inflecting and compounding languages; I.5.1 [PatternRecognition; Language models; Languages; LVCSR; Models—statistical General Terms; Morfessor; morpheme; morphologically rich languages; n-gram models; Performance Additional Key Words and Phrases; subword-based language modeling; Turkish
	URL: http://www-speech.sri.com/cgi-bin/run-distill?papers/acm2007-morph-asr.ps.gz http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.3581
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18	Citizenship PGCE Assignment 1
	Samuel Marlow
	In: http://www.citized.info/pdf/students/samuel_marlow.pdf
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19	The Advanced Speaker: An Overview of the Issues in Heritage Language
	Maria M. Carreira
	In: http://international.ucla.edu/media/files/CarreiraPositionPaperFinal.pdf
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20	Language Profile Dear Colleagues
	Cathy Haghighat
	In: http://languageprofiles.com/wp-content/uploads/2008/03/fall2001contacthungarianexcerpt.pdf
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