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Hits 1 – 20 of 54

1	Multilingual Number Transcription for Text-to-Speech Conversion
	R. San-segundo; J. M. Montero; M. Giurgiu...
	In: http://www.cstr.ed.ac.uk/downloads/publications/2013/ssw8_PS1-8_San-Segundo.pdf (2013)
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2	DiarTk: An open source toolkit for research in multistream speaker diarization and its application to meetings recordings
	Deepu Vijayasenan; Fabio Valente
	In: http://publications.idiap.ch/downloads/papers/2012/Vijayasenan_INTERSPEECH2012_2012.pdf (2012)
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3	Recovering Capitalization and Punctuation Marks on Speech Transcriptions
	O Batista; Nuno Mamede
	In: http://www.propor2012.org/theses/FBatista_PROPOR2012-PhD.pdf (2011)
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4	Clin Linguist Phon Downloaded from informahealthcare.com by University of Minnesota For personal use only.
	Vox Humana; Benjamin Munson; Jan Edwards...
	In: http://www.tc.umn.edu/%7Emunso005/MunsonEtAl2010.pdf (2009)
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5	partial automation
	Ben Serridge; Luciana Castro
	In: http://isca-speech.org/archive_open/archive_papers/exling2008/exl8_197.pdf (2008)
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6	Methodological questions in studying consonant acquisition
	Jan Edwards; Mary E. Beckman
	In: http://www.ling.ohio-state.edu/%7Eedwards/EdwardsBeckman2008proofsCLP.pdf (2008)
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7	Making use of bilingual interview data: Some expressions from the field
	Nelofer Halai
	In: http://www.nova.edu/ssss/QR/QR12-3/halai.pdf (2007)
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8	Integrated Linguistic Resources for Language Exploitation Technologies
	Stephanie Strassel Christopher; Christopher Cieri; Andrew Cole...
	In: http://papers.ldc.upenn.edu/LREC2006/LREC_2006_GALE_Paper.pdf (2006)
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9	Integrated linguistic resources for language exploitation technologies
	Stephanie Strassel; Christopher Cieri; Andrew Cole...
	In: http://www.cs.brandeis.edu/~marc/misc/proceedings/lrec-2006/pdf/745_pdf.pdf (2006)
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10	Transitioning from analog to digital audio recording in childhood speech sound disorders
	Lawrence D. Shriberg; Jane L. Mcsweeny; Bruce E...
	In: http://www.waisman.wisc.edu/phonology/pubs/PUB4.pdf (2005)
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11	Marijn van Dijk a,∗, Paul van Geert b
	In: http://www.paulvangeert.nl/publications_files/Disentagling pdf.pdf (2004)
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12	Abstract The Buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability
	Mark A. Pitt A; Keith Johnson B; Elizabeth Hume B...
	In: http://linguistics.berkeley.edu/~kjohnson/papers/Pitt_et_al.pdf (2004)
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13	The Arabic noun phrase. A minimalist approach
	Joost Merijn Kremers
	In: http://www.lotpublications.nl/publish/articles/000410/bookpart.pdf (2003)
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14	The LIMSI Broadcast News Transcription System
	Jean-luc Gauvain; Lori Lamel; Gilles Adda
	In: ftp://tlp.limsi.fr/public/spcH4_limsi.ps.Z (2002)
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15	Competing Creole Transcripts on Trial
	Peter Patrick Dept; Peter L. Patrick; Samuel W. Buell
	In: http://privatewww.essex.ac.uk/~patrickp/papers/CreoleTranscripts.pdf (2000)
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16	Fast Speaker Change Detection for Broadcast News Transcription and Indexing
	Daben Liu; Francis Kubala
	In: http://www.ee.columbia.edu/~dpwe/papers/LiuK99-spkrseg.pdf (1999)
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17	Title Specification of orthographic transcription and lexicon conventions Status Final Deliverable SD1.3.2 Work Package WP 1 Task Task 1.3
	Period Covered T T; Author(s Francesco Senia; Jeroen G. Van Velden...
	In: http://www.speechdat.org/speechdat/deliverables/public/sd132v24.pdf (1997)
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18	Quantitative Classification of Conversational Language Using Artificial Neural Networks
	Sameer Singh
	In: http://www.dcs.ex.ac.uk/research/pann/pdf/pann_SS_040.pdf (1997)
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19	Neural Networks In Spontaneous Speech Assessment Of Dysphasic Patients
	Sameer Singh
	In: http://www.dcs.ex.ac.uk/research/pann/pdf/pann_SS_041.pdf (1996)
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20	INTERSPEECH 2010 Predicting human perception and ASR classification of word-final [t] by its acoustic sub-segmental properties
	Mirjam Ernestus; Wim Van Dommelen; Jacques Koreman
	In: http://lands.let.kun.nl/literature/schuppler.2010.2.pdf
	Abstract: This paper presents a study on the acoustic sub-segmental properties of word-final /t / in conversational standard Dutch and how these properties contribute to whether humans and an ASR system classify the /t / as acoustically present or absent. In general, humans and the ASR system use the same cues (presence of a constriction, a burst, and alveolar friction), but the ASR system is also less sensitive to fine cues (weak bursts, smoothly starting friction) than human listeners and misled by the presence of glottal vibration. These data inform the further development of models of human and automatic speech processing.
	Keyword: Automatic Transcription; Dutch; Human speech Perception; Index Terms; Sub-segmental Acoustic Properties
	URL: http://lands.let.kun.nl/literature/schuppler.2010.2.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.7897
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