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The Pennsylvania State University CiteSeerX Archives (10)
Sachin S. Kajarekar (9)
Andreas Stolcke (8)
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Luciana Ferrer (5)
Jing Zheng (3)
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Hits 1 – 10 of 10
1
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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2
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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3
SRI’s 2004 NIST speaker recognition evaluation system
Sachin S. Kajarekar
;
Luciana Ferrer
;
Elizabeth Shriberg
...
In: http://www.speech.sri.com/papers/icassp2005-spkr-system.pdf (2005)
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4
SRI’s 2004 NIST speaker recognition evaluation system
Sachin S. Kajarekar
;
Luciana Ferrer
;
Elizabeth Shriberg
...
In: http://www-speech.sri.com/papers/icassp2005-spkr-system.ps.gz (2005)
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5
The SRI NIST 2008 speaker recognition evaluation system
Sachin S Kajarekar
;
Nicolas Scheffer
;
Martin Graciarena
...
In: https://www.sri.com/sites/default/files/publications/the_sri_nist_2008_speaker_recognition_evaluation_system.pdf
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6
SRI’s 2004 NIST speaker recognition evaluation system
Sachin S. Kajarekar
;
Luciana Ferrer
;
Elizabeth Shriberg
;
Kemal Sonmez
;
Andreas Stolcke
;
Jing Zheng
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2005-spkr-system.ps.gz
Abstract:
This paper describes our recent efforts in exploring longerrange features and their statistical modeling techniques for speaker recognition. In particular, we describe a system that uses discriminant features from cepstral coefficients, and systems that use discriminant models from word n-grams and syllable-based NERF n-grams. These systems together with a cepstral baseline system are evaluated on the 2004 NIST speaker recognition evaluation dataset. The effect of the development set is measured using two different datasets, one from Switchboard databases and another from the FISHER database. Results show that the difference between the development and evaluation sets affects the performance of the systems only when more training data is available. Results also show that systems using longer-range features combined with the baseline result in about a 31 % improvement with 1-side training over the baseline system and about a 61 % improvement with 8-side training over the baseline system. 1.
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.58
http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2005-spkr-system.ps.gz
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7
ISCA Archive Speaker Verification Based on Broad Phonetic Categories
Sachin S. Kajarekar
;
Hynek Hermansky
In: http://isca-speech.org/archive_open/archive_papers/odyssey/odys_201.pdf
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8
ACROSS-PHONE VARIABILITY AND DIAGONAL TERM IN JOINT FACTOR ANALYSIS FOR SPEAKER RECOGNITION
Sachin S. Kajarekar
In: http://www.speech.sri.com/papers/icassp2010-phone-jfa.pdf
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9
NAP AND WCCN: COMPARISON OF APPROACHES USING MLLR-SVM SPEAKER VERIFICATION SYSTEM
Sachin S. Kajarekar
;
Andreas Stolcke
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2007-nap-wccn.ps.gz
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10
NAP AND WCCN: COMPARISON OF APPROACHES USING MLLR-SVM SPEAKER VERIFICATION SYSTEM
Sachin S. Kajarekar
;
Andreas Stolcke
In: http://www.speech.sri.com/papers/icassp2007-nap-wccn.ps.gz
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