publication . Article . 2014

Convolutional Neural Networks for Speech Recognition

Ossama Abdel-Hamid; Abdel-rahman Mohamed; Hui Jiang; Li Deng; Gerald Penn; Dong Yu;
Open Access
  • Published: 16 Jul 2014
Abstract
Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structur...
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arXiv: Computer Science::SoundComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Computer Science::Neural and Evolutionary Computation
free text keywords: Electrical and Electronic Engineering, Acoustics and Ultrasonics, Computer Science (miscellaneous), Computational Mathematics, Speech recognition, Mixture model, Artificial neural network, Time delay neural network, TIMIT, Artificial intelligence, business.industry, business, Pattern recognition, Word error rate, Hidden Markov model, Deep learning, Convolutional neural network, Computer science
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