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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)
Abstract: We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data. We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter). We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR). We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features. Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.7600
http://www.mde.zcu.cz/data/Kolar06tsd.pdf
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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)
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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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