Cluster adaptive training for deep neural network

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2015-August, Issue , 2015, Pages 4325-4329

  Tan, Tian ^a   Qian, Yanmin ^a   Yin, Maofan ^a   Zhuang, Yimeng ^a   Yu, Kai ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Adaptation; Cluster Adaptive Training; Deep Neural Network

Indexed keywords

AUDIO SIGNAL PROCESSING; MATHEMATICAL TRANSFORMATIONS; SPEECH COMMUNICATION;

ACOUSTIC CONDITIONS; ADAPTATION; ADAPTIVE TRAINING; CLUSTER ADAPTIVE TRAINING; PERFORMANCE DEGRADATION; RESEARCH INTERESTS; UNSUPERVISED ADAPTATION; VECTOR REPRESENTATIONS;

DEEP NEURAL NETWORKS;

EID: 84946083667     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2015.7178787     Document Type: Conference Paper

References (21)

1. George E Dahl, Dong Yu, Li Deng, and Alex Acero, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, Audio, Speech, and Language Processing, IEEE Transactions on, vol. 20, no. 1, pp. 30-42, 2012
2. Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdelrahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Signal Processing Magazine, IEEE, vol. 29, no. 6, pp. 82-97, 2012
3. Xie Chen, Adam Eversole, Gang Li, Dong Yu, and Frank Seide, Pipelined back-propagation for context-dependent deep neural networks., in INTERSPEECH, 2012
4. Yan Huang, Dong Yu, Chaojun Liu, and Yifan Gong, A comparative analytic study on the Gaussian mixture and context dependent deep neural network hidden markov models, submitted to the Interspeech, 2014
5. Jean-Luc Gauvain and Chin-Hui Lee, Maximum a posteriori estimation for multivariate Gaussian mixture observations of markov chains., IEEE Transactions on Speech and Audio Processing, vol. 2, no. 2, pp. 291-298, 1994
6. Christopher J Leggetter and Philip C Woodland, Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models, Computer Speech &Language, vol. 9, no. 2, pp. 171-185, 1995
7. Mark JF Gales, Maximum likelihood linear transformations for hmm-based speech recognition, Computer speech &language, vol. 12, no. 2, pp. 75-98, 1998
8. Dong Yu, Kaisheng Yao, Hang Su, Gang Li, and Frank Seide, Kl-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition, in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013, pp. 7893-7897
9. Kaisheng Yao, Dong Yu, Frank Seide, Hang Su, Li Deng, and Yifan Gong, Adaptation of context-dependent deep neural networks for automatic speech recognition., in SLT, 2012, pp. 366-369
10. Frank Seide, Gang Li, Xie Chen, and Dong Yu, Feature engineering in context-dependent deep neural networks for conversational speech transcription, in Automatic Speech Recognition and Understanding (ASRU), 2011 IEEE Workshop on. IEEE, 2011, pp. 24-29
11. Roberto Gemello, Franco Mana, Stefano Scanzio, Pietro Laface, and Renato De Mori, Linear hidden transformations for adaptation of hybrid ann/hmm models, Speech Communication, vol. 49, no. 10, pp. 827-835, 2007
12. Bo Li and Khe Chai Sim, Comparison of discriminative input and output transformations for speaker adaptation in the hybrid nn/hmm systems, 2010
13. Tsubasa Ochiai, Shigeki Matsuda, Xugang Lu, Chiori Hori, and Shigeru Katagiri, Speaker adaptive training using deep neural networks, in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 6349-6353
14. George Saon, Hagen Soltau, David Nahamoo, and Michael Picheny, Speaker adaptation of neural network acoustic models using i-vectors, in Automatic Speech Recognition and Understanding (ASRU), 2013 IEEEWorkshop on. IEEE, 2013, pp. 55-59
15. Andrew Senior and Ignacio Lopez-Moreno, Improving dnn speaker independence with i-vector inputs, in Proc. ICASSP, 2014
16. Vishwa Gupta, Patrick Kenny, Pierre Ouellet, and Themos Stafylakis, I-vector-based speaker adaptation of deep neural networks for French broadcast audio transcription, in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 6334-6338
17. Ossama Abdel-Hamid and Hui Jiang, Fast speaker adaptation of hybrid nn/hmm model for speech recognition based on discriminative learning of speaker code, in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013, pp. 7942-7946
18. Shaofei Xue, Ossama Abdel-Hamid, Hui Jiang, and Lirong Dai, Direct adaptation of hybrid dnn/hmm model for fast speaker adaptation in lvcsr based on speaker code, in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 6339-6343
19. Mark JF Gales, Cluster adaptive training for speech recognition., in ICSLP, 1998, vol. 1998, pp. 1783-1786
20. Mark JF Gales, Cluster adaptive training of hidden markov models, Speech and Audio Processing, IEEE Transactions on, vol. 8, no. 4, pp. 417-428, 2000
21. Roland Kuhn, Patrick Nguyen, Jean-Claude Junqua, Lloyd Goldwasser, Nancy Niedzielski, Steven Fincke, Ken Field, and Matteo Contolini, Eigenvoices for speaker adaptation