Seminar: Dong Yu
Why Deep Neural Networks Are Promising for Speech Recognition
Jun 25, 2012
from 11:00 AM to 12:30 PM
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Recently we have proposed and developed the context-dependent deep neural network (DNN) hidden Markov model (CD-DNN-HMM) for large vocabulary speech recognition (LVSR) and demonstrated its superior performance on several benchmark tasks. In this talk I will share the observations and thoughts we have in understanding why DNNs can be more powerful than the shallow neural networks and why CD-DNN-HMMs can outperform the conventional CD-GMM-HMM system and earlier ANN/HMM hybrid systems. At the end of the talk I will discuss how CD-DNN-HMM can be further improved to achieve even better recognition accuracy.
Dr. Dong Yu joined Microsoft Corporation in 1998 and Microsoft Speech Research Group in 2002, where he is a researcher. His recent work focuses on deep learning and its application to large vocabulary speech recognition. The context-dependent deep neural network hidden Markov model (CD-DNN-HMM) he co-proposed and developed has been seriously challenging the dominant position of the conventional GMM based system for large vocabulary speech recognition.
Dr. Dong Yu has published around 100 papers in speech processing and machine learning and is the inventor/coinventor of more than 40 granted/pending patents. He is currently serving as an associate editor of IEEE transactions on audio, speech, and language processing (2011-) and has served as an associate editor of IEEE signal processing magazine (2008-2011) and the lead guest editor of IEEE transactions on audio, speech, and language processing - special issue on deep learning for speech and language processing (2010-2011).