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--- projects:mdnn:start [2015/02/11 16:35] – created hj
+++ projects:mdnn:start [2015/10/01 13:58] (current) – hj
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 ===== multi-DNN Acoustic models for Speech Recognition =====
+{{ :screen_shot_2015-02-11_at_11.38.24_am.png?0x300 |}}
+\\
+\\
+We propose a novel DNN-based acoustic modeling framework for speech
+recognition, where the posterior probabilities of HMM states are
+computed from multiple DNNs (mDNN), instead of a single large
+DNN, for the purpose of parallel training towards faster turnaround.
+In the proposed mDNN method all tied HMM states are
+first grouped into several disjoint clusters based on data-driven
+methods. Next, several hierarchically structured DNNs are trained
+separately in parallel for these clusters using multiple computing
+units (e.g. GPUs).
+\\
+**Reference**: \\
+[1] P. Zhou, **H. Jiang**, L. Dai, Y. Hu and Q. Liu, "[[http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7010902&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6570655%2F7055953%2F07010902.pdf%3Farnumber%3D7010902|A State-Clustering based Multiple Deep Neural Networks Modelling Approach for Speech Recognition]]," //IEEE/ACM Trans. on Audio, Speech and Language Processing//, pp.631-642, Vol. 23, No. 4, April 2015.\\
+[2] P. Zhou, L. Dai, **H. Jiang**, "Sequence Training of Multiple Deep Neural Networks for Better Performance and Faster Training Speed," //Proc. of of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'14)//, Florence, Italy, May 2014. \\
+[3] P. Zhou, C. Liu, Q. Liu, L. Dai and **H. Jiang**, "A cluster-based multiple deep neural networks method for large vocabulary continuous speech recognition," //Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'13)//, Vancouver, Canada, 2013.