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The Pennsylvania State University CiteSeerX Archives (54)
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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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