Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hintona b, Li Dengc, Dong Yud e, George E. Dahla b, Abdelrahman Mohameda f, Navdeep Jaitlya b, Andrew Seniorg h, Vincent Vanhouckei j, Patrick Nguyeni j, Tara N. Sainathk l, Brian E. D. Kingsburym
- aUniversity of Toronto, Canada
- b[Computer Science, Univ. Toronto, Toronto, Canada]
- cDepartment of Electrical and Computer Engineering, University of Waterloo, ONT, Canada
- dMicrosoft Corporation, USA
- eMicrosoft Research Redmond Washington USA
- fDepartment of Computer Science, University of Toronto, Toronto, M5S 3G4, Canada
- gGoogle, Inc., USA
- hGoogle Inc., USA
- iGoogle Research, Mountain View, CA, USA
- jResearch, Google, Mountainview, California USA
- kIBM Thomas J. Watson Research Center, USA
- lIBM Thomas J Watson Research Center, USA
- mElectrical engineering, Michigan State University, East Lansing, USA
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Abstract
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.