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1
Spoken language recognition based on senone posteriors
In: https://www.sri.com/sites/default/files/publications/spoken_language_recognition_based_on_senone_posteriors.pdf (2014)
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2
The SRI NIST 2010 speaker recognition evaluation system
In: http://www.speech.sri.com/papers/icassp2011-sre10-system.pdf (2011)
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3
Speaker recognition with session variability normalization based on MLLR adaptation transforms
In: http://www.icsi.berkeley.edu/pubs/speech/ieee-aslp2007-mllrsvm.ps.pdf (2007)
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4
SRI’s 2004 NIST speaker recognition evaluation system
In: http://www.speech.sri.com/papers/icassp2005-spkr-system.pdf (2005)
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5
SRI’s 2004 NIST speaker recognition evaluation system
In: http://www-speech.sri.com/papers/icassp2005-spkr-system.ps.gz (2005)
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=10.1.1.70.7788
http://www-speech.sri.com/papers/icassp2005-spkr-system.ps.gz
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6
Modeling NERFs for Speaker Recognition
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/odyssey2004-nerfs.ps.gz (2004)
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7
SVM Modeling of SNERF-Grams for Speaker Recognition
In: http://www.speech.sri.com/cgi-bin/run-distill?papers/icslp2004-snerfs.ps.gz (2004)
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8
Gadde, “Speaker recognition using prosodic and lexical features
In: http://www-speech.sri.com/papers/asru2003-spkrid.ps.gz (2003)
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9
Modeling Duration Patterns for Speaker Recognition
In: http://www-speech.sri.com/papers/eurospeech2003-spkrdur.ps.gz (2003)
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10
Gadde, “Speaker recognition using prosodic and lexical features
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/asru2003-spkrid.ps.gz (2003)
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11
Modeling duration patterns for speaker recognition," presented at Eurospeech
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/eurospeech2003-spkrdur.ps.gz (2003)
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12
Prosodic Knowledge Sources For Automatic Speech Recognition
In: http://www-speech.sri.com/papers/icassp2003-prosodyasr.ps.gz (2003)
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13
Training a prosody-based dialog act tagger from unlabeled data
In: http://www-speech.sri.com/cgi-bin/run-distill?ftp:papers/icassp2003-dialog.ps.gz (2003)
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14
Prosodic knowledge sources for automatic speech recognition
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2003-prosodyasr.ps.gz (2003)
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15
Training a Prosody-Based Dialog Act Tagger from Unlabeled Data
In: http://www-speech.sri.com/papers/icassp2003-dialog.ps.gz (2003)
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16
The SRI NIST 2008 speaker recognition evaluation system
In: https://www.sri.com/sites/default/files/publications/the_sri_nist_2008_speaker_recognition_evaluation_system.pdf
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17
Class-based score combination for speaker recognition
In: https://www.sri.com/sites/default/files/publications/class-dependent_score_combination_for_speaker_recog.pdf
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18
SRI’s 2004 NIST speaker recognition evaluation system
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/icassp2005-spkr-system.ps.gz
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19
A COMPARISON OF APPROACHES FOR MODELING PROSODIC FEATURES IN SPEAKER RECOGNITION
In: http://www.speech.sri.com/cgi-bin/run-distill?papers/icassp2010-ferrer.ps.gz
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20
Adaptive and Discriminative Modeling for Improved Mispronunciation Detection
In: http://www.speech.sri.com/papers/Franco_Ferrer_Bratt_IS_ADEPT.pdf
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