[1] Cui X, Xue J, Dognin P L, et al. Acoustic modeling with bootstrap and restructuring for low-resourced languages[C]//Interspeech. 2010: 2 974-2 977.[2] Vu N T, Schlippe T, Kraus F, et al. Rapid bootstrapping of five eastern european languages using the rapid language adaptation toolkit[C]//Interspeech. 2010: 865-868.[3] Rabiner L R. A Tutorial on hidden markov models and selected applications in speech recognition[J].Proceedings of IEEE, 1989, 77(2):257-286.[4] Davis S, Mermelstein P. Comparison of parametric representations formonosyllable word recognition in continuously spoken sentences[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1980, 28(4):357-366.[5] Povey D, Burget L, Agarwal M, et al. Subspace Gaussian mixture models for speech recognition[C]//Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. IEEE, 2010: 4 330-4 333.[6] Povey D, Burget L, Agarwal M, et al. The subspace Gaussian mixture model: a structured model for speech recognition[J]. Computer Speech and Language, 2011, 25(2):404-439.[7] Dahl G, Yu D, Deng L, et al. Context-dependent pre-trained deep neural networks for large vocabulary speech recognition [J]. IEEE Trans on Audio, Speech and Language Processing, 2012, 20(1): 30-42.[8] Seide F, Li G, Yu D. Conversational speech transcription using context-dependent deep neural networks//Interspeech. 2011: 437-440.[9] Normandin Y. Hidden Markov models, maximum mutual information estimation, and the speech recognition problem . Canada: McGill University, 1991.[10] He X D, Deng L, Chou W. Discriminative learning in sequential pattern recognition[J]. IEEE Signal Processing Magazine, 2008, 14(1):14-36.[11] Yu D, Seltzer M L. Improved bottleneck features using pretrained deep neural networks[C]//INTERSPEECH. 2011: 237-240.[12] IARPA. OpenKWS13 keyword search evaluation . (2013-01-25) . http://www.nist.gov/itl/iad/mig/upload/OpenKWS13.[13] 单煜翔. 高效大词汇量连续语音识别解码算法研究与工程化实现[D]. 北京: 清华大学, 2012.[14] Povey D, Ghoshal A, Boulianne G, et al. The Kaldi speech recognition toolkit[C]//Proc ASRU. 2011: 1-4.[15] 钱彦旻. 低数据资源条件下的语音识别技术新方法研究[D]. 北京: 清华大学, 2013.[16] Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups[J]. IEEE, Signal Processing Magazine, 2012, 29(6): 82-97.[17] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1 527-1 554.[18] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.[19] Fontaine V, Ris C, Boite J M. Nonlinear discriminant analysis for improved speech recognition[C]//Eurospeech. 1997.[20] Grézl F, Karafiát M, Kontár S, et al. Probabilistic and bottle-neck features for LVCSR of meetings[C]//Proc ICASSP. 2007(4):757-761. |