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1
Addressee detection for dialog systems using temporal and spectral dimensions of speaking style,” in
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
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
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
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
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
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
In: http://www.speech.sri.com/people/gokhan/pubs/IS072.pdf (2007)
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8
Crossgenre feature comparisons for spoken sentence segmentation
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
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
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
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
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
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
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
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
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
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
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
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
In: http://www.icsi.berkeley.edu/~yangl/icassp2005-da-seg-class.pdf (2005)
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