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Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs.
J. Godfrey, E. Holliman, and J. McDaniel, “SWITCHBOARD: Telephone speech corpus for research and development,” in
1992
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D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,”
2003
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S. Shalev-Shwartz, Y. Singer, and N. Srebro, “Pegasos: Primal estimated sub-gradient solver for SVM,” in
2007
Earlier work this paper cites.
A. S. Park and J. R. Glass, “Unsupervised pattern discovery in speech,”
2008
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in
2008
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M. Dredze, A. Jansen, G. Coppersmith, and K. Church, “NLP on spoken documents without ASR,” in
2010
Earlier work this paper cites.
R. Řehůřek and P. Sojka, “Software Framework for Topic Modelling with Large Corpora,” in
2010
Earlier work this paper cites.
T. J. Hazen, “MCE Training Techniques for Topic Identification of Spoken Audio Documents,”
2011
Earlier work this paper cites.
A. Jansen and B. Van Durme, “Efficient spoken term discovery using randomized algorithms,” in
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,”
2011
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
C.-y. Lee and J. Glass, “A nonparametric bayesian approach to acoustic model discovery,” in
2012
Cited alongside, same era.
M. D. Zeiler, “Adadelta: an adaptive learning rate method,”
2012
Cited alongside, same era.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,”
2012
Cited alongside, same era.
P. Xu and R. Sarikaya, “Convolutional neural network based triangular CRF for joint intent detection and slot filling,” in
2013
Cited alongside, same era.
2013
Cited alongside, same era.
2014
Later among the works it cites.
C. May, F. Ferraro, A. McCree, J. Wintrode, D. Garcia-Romero, and B. Van Durme, “Topic identification and discovery on text and speech,” in
2015
Later among the works it cites.
C. Liu, P. Xu, and R. Sarikaya, “Deep contextual language understanding in spoken dialogue systems.” in
2015
Later among the works it cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
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T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in
2013
Cited alongside, same era.
J. Wintrode and S. Khudanpur, “Limited resource term detection for effective topic identification of speech,” in
2014
Cited alongside, same era.
M.-h. Siu, H. Gish, A. Chan, W. Belfield, and S. Lowe, “Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery,”
2014
Cited alongside, same era.
Y. Kim, “Convolutional neural networks for sentence classification,”
2014
Cited alongside, same era.
J. Nam, J. Kim, E. L. Mencía, I. Gurevych, and J. Fürnkranz, “Large-scale multi-label text classification - revisiting neural networks,” in
2014
Cited alongside, same era.
F. Chollet, “Keras,”
2015
Later among the works it cites.
L. Ondel, L. Burget, and J. Černockỳ, “Variational inference for acoustic unit discovery,” in
2016
Later among the works it cites.
2016
Later among the works it cites.
S. Kesiraju, R. Pappagari, L. Ondel, L. Burget, N. Dehak, S. Khudanpur, J. Černockỳ, and S. Gangashetty, “Topic identification of spoken documents using unsupervised acoustic unit discovery,” in
2017
Closest in time.
C. Liu, J. Yang, M. Sun, S. Kesiraju, A. Rott, L. Ondel, P. Ghahremani, N. Dehak, L. Burget, and S. Khudanpur, “An empirical evaluation of zero resource acoustic unit discovery,” in
2017
Closest in time.