Citation: | ZHANG Yike, ZHANG Pengyuan, YAN Yonghong, “Language Model Score Regularization for Speech Recognition,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 604-609, 2019, doi: 10.1049/cje.2019.03.015 |
J. Xu, J. Pan and Y. Yan, "Agglutinative language speech recognition using automatic allophone deriving", Chinese Journal of Electronics, Vol.25, No.2, pp.328-333, 2016.
|
J. Su, Z. Wang, Q. Wu, et al., " A topic-triggered translation model for statistical machine translation", Chinese Journal of Electronics, Vol.26, No.1, pp.65-72, 2017.
|
P. Li, L. Peng and J. Wen, "Rejecting character recognition errors using CNN based confidence estimation", Chinese Journal of Electronics, Vol.25, No.3, pp.520-526, 2016.
|
Z. Yang, F. Yao, K. Fan, et al., "Text dimensionality reduction with mutual information preserving mapping", Chinese Journal of Electronics, Vol.26, No.5, pp.919-925, 2017.
|
F. Jelinek, "Interpolated estimation of markov source parameters from sparse data", Proc. of the workshop on Pattern Recognition in Practice, Amsterdam, North-Holland, Netherlands, pp.381-397, 1980.
|
S.M. Katz, "Estimation of probabilities from sparse data for the language model component of a speech recognizer", IEEE Transactions on Acoustics Speech and Signal Processing, Vol.35, No.3, pp.400-401, 1987.
|
S.F. Chen and J. Goodman, "An empirical study of smoothing techniques for language modeling", Proc. of the 34th Annual Meeting on Association for Computational Linguistics (ACL), Santa Cruz, California, USA, pp.310-318, 1999.
|
J.T. Goodman, "A bit of progress in language modeling", Computer Speech and Language, Vol.15, No.4, pp.403-434, 2001.
|
I. Pouzyrevsky, "Scalable modified Kneser-Ney language model estimation", Proc. of the 51th Annual Meeting on Association for Computational Linguistics (ACL), Sofia, Bulgaria, pp.690-696, 2013.
|
Y. Bengio, R. Ducharme, P. Vincent, et al., "A neural probabilistic language model", Journal of Machine Learning Research, Vol.3, No.6, pp.1137-1155, 2003.
|
T. Mikolov, M. Karafit, L. Burget, et al., "Recurrent neural network based language model", Proc. of Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Chiba, Japan, pp.1045-1048, 2010.
|
M. Sundermeyer, H. Ney and R. Schluter, "From feedforward to recurrent LSTM neural networks for language modeling", IEEE Transactions on Audio Speech and Language Processing, Vol.23, No.3, pp.517-529, 2015.
|
W.D. Mulder, S. Bethard and M.F. Moens, "A survey on the application of recurrent neural networks to statistical language modeling", Computer Speech and Language, Vol.30, No.1, pp.61-98, 2015.
|
Y. Gal and Z. Ghahramani, "A theoretically grounded application of dropout in recurrent neural networks", Proc. of the 30th Annual Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, pp.1019-1027, 2016.
|
J.T. Chien and Y.C. Ku, "Bayesian recurrent neural network language model", Proc. of IEEE Workshop on Spoken Language Technology (SLT), South Lake Tahoe, Nevada, USA, pp.206-211, 2014.
|
E. Arisoy, A. Sethy, B. Ramabhadran, et al., "Bidirectional recurrent neural network language models for automatic speech recognition", Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Queensland, Australia, pp.5421-5425, 2015.
|
X. Chen, A. Ragni, X. Liu, et al., "Investigating bidirectional recurrent neural network language models for speech recognition", Proc. of Conference of the International Speech Communication Association (INTERSPEECH), Stockholm, Sweden, pp.269-273, 2017.
|
H. Ney, U. Essen and R. Kneser, "On structruing porbabilittic dependences in stochastic language modelling", Computer Speech and Language, Vol.8, No.1, PP.1-38, 199.
|
R. Pickhardt, T. Gottron, M. Körner, et al., "A generalized language model as the combination of skipped n-grams and modified Kneser-Ney smoothing", Proc. of the 52nd Annual Meeting on Association for Computational Linguistics (ACL), Baltimore, Maryland, USA, pp.1145-1154, 2014.
|
N. Shazeer, J. Pelemans and C. Chelba, "Sparse non-negative matrix language modeling for skip-grams", Proc. of Conference of the International Speech Communication Association (INTERSPEECH), Dresden, Germany, pp.1428-1432, 2015.
|
D. Guthrie, B. Allison, W. Liu, et al., "A closer look at skip-gram modelling", In Proc. of the 5th International Conference on Language Resources and Evaluation, Genoa, Italy, pp.1222-1225, 2006.
|
Z. Xie, S.I. Wang, J. Li, et al., "Data noising as smoothing in neural network language models", In Proc, of the 5th International Conference on Learning Representations (ICLR), Toulon, France, pp.1-11,2017.
|
M. Schuster and K.K. Paliwal, "Bidirectional recurrent neural networks", IEEE Transactions on Signal Processing, Vol.45, No.11, pp.2673-2681, 1997.
|
D. Povey, A. Ghoshal, G. Boulianne, et al., "The kaldi speech recognition toolkit", Proc. of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Hawaii, USA, 2011.
|
A. Stolcke, "Srilm-An extensible language modeling toolkit", Proc. of Conference of the International Speech Communication Association (INTERSPEECH), Denver, Colorado, USA, pp.901-904, 2002.
|
F. Seide and A. Agarwal, "CNTK:Microsoft's open-source deep-learning toolkit", Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp.2135-2135, 2016.
|