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.

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In  IEEE Signal Processing Magazine

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