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Hits 1 – 20 of 171
1
Addressee detection for dialog systems using temporal and spectral dimensions of speaking style,” in
Elizabeth Shriberg
;
Andreas Stolcke
;
Suman Ravuri
In: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/paper-32.pdf (2013)
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2
Pitch-gesture modeling using subband autocorrelation change detection
Malcolm Slaney
;
Elizabeth Shriberg
;
Jui-ting Huang
In: http://www.slaney.org/malcolm/Microsoft/Slaney2013%28ToneWithoutPitch%29.pdf (2013)
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3
The SRI NIST 2010 speaker recognition evaluation system
Nicolas Scheffer
;
Luciana Ferrer
;
Martin Graciarena
...
In: http://www.speech.sri.com/papers/icassp2011-sre10-system.pdf (2011)
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4
Comparing the contributions of context and prosody in text-independent dialog act recognition
Kornel Laskowski
;
Elizabeth Shriberg
In: http://www.cs.cmu.edu/~kornel/pubs/0005374.pdf (2010)
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5
S.: Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification
Gokhan Tur
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.speech.sri.com/papers/eurospeech2007-lexical-sid.ps.gz (2007)
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6
S.: Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification
Gokhan Tur
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.speech.sri.com/papers/IS07-gokhan-p1171.pdf (2007)
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7
S.: Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification
Gokhan Tur
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.speech.sri.com/people/gokhan/pubs/IS072.pdf (2007)
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8
Crossgenre feature comparisons for spoken sentence segmentation
Sebastien Cuendet
;
Dilek Hakkani-tur
;
James Fung
...
In: http://pageperso.lif.univ-mrs.fr/%7Ebenoit.favre/papers/favre_ijsc2007.pdf (2007)
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9
S.: Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification
Gokhan Tur
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/eurospeech2007-lexical-sid.ps.gz (2007)
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10
Speaker recognition with session variability normalization based on MLLR adaptation transforms
Andreas Stolcke
;
Senior Member
;
Sachin S. Kajarekar
...
In: http://www.icsi.berkeley.edu/pubs/speech/ieee-aslp2007-mllrsvm.ps.pdf (2007)
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11
Higher-Level Features in Speaker Recognition,” in Speaker Classification I
Elizabeth Shriberg
In: http://www.speech.sri.com/papers/bookchapter-hlf-LNAI07.pdf (2007)
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12
Combining prosodic, lexical and cepstral systems for deceptive speech detection
Martin Graciarena
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.speech.sri.com/papers/icassp2006-deception.ps.gz (2006)
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13
Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings
Jáchym Kolár
;
Elizabeth Shriberg
;
Yang Liu
In: http://www.mde.zcu.cz/data/Kolar06tsd.pdf (2006)
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14
Combining Prosodic, Lexical and Cepstral Systems for Deceptive Speech Detection
Martin Graciarena
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.cs.columbia.edu/~julia/papers/icassp06_deception.pdf (2006)
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15
The contribution of cepstral and stylistic features to SRI’s 2005 NIST speaker recognition evaluation system
Elizabeth Shriberg
;
Sachin S. Kajarekar
;
Andreas Stolcke
...
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2006-spkr-system.ps.gz (2006)
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16
Combining Prosodic, Lexical and Cepstral Systems for Deceptive Speech Detection
Martin Graciarena
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2006-deception.ps.gz (2006)
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17
Combining prosodic, lexical and cepstral systems for deceptive speech detection
Martin Graciarena
;
Elizabeth Shriberg
;
Andreas Stolcke
...
In: http://www.cs.columbia.edu/~frank/papers/icassp06_deception.pdf (2006)
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18
Does active learning help automatic dialog act taggin in meeting data
Yang Liu
;
Elizabeth Shriberg
;
Andreas Stolcke
In: http://http.icsi.berkeley.edu/ftp/global/pub/speech/papers/eurospeech2005-active.pdf (2005)
Abstract:
Knowledge of Dialog Acts (DAs) is important for the automatic understanding and summarization of meetings. Current approaches rely on a lot of hand labeled data to train automatic taggers. One approach that has been successful in reducing the amount of training data in other areas of NLP is active learning. We ask if active learning with lexical cues can help for this task and this domain. To better address this question, we explore active learning for two different types of DA models – hidden Markov models (HMMs) and maximum entropy (maxent). 1.
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.9259
http://http.icsi.berkeley.edu/ftp/global/pub/speech/papers/eurospeech2005-active.pdf
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19
Does active learning help automatic dialog act taggin in meeting data
Yang Liu
;
Elizabeth Shriberg
;
Andreas Stolcke
In: http://www.speech.sri.com/papers/eurospeech2005-active.ps.gz (2005)
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20
Automatic Dialog Act Segmentation and Classification in Multiparty Meetings
Jeremy Ang
;
Yang Liu
;
Elizabeth Shriberg
In: http://www.icsi.berkeley.edu/~yangl/icassp2005-da-seg-class.pdf (2005)
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