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Title: Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition

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Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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Country of Publication:
United States

Citation Formats

Stevenson, G A. Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition. United States: N. p., 2016. Web. doi:10.2172/1289367.
Stevenson, G A. Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition. United States. doi:10.2172/1289367.
Stevenson, G A. Thu . "Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition". United States. doi:10.2172/1289367.
title = {Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition},
author = {Stevenson, G A},
abstractNote = {},
doi = {10.2172/1289367},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jul 21 00:00:00 EDT 2016},
month = {Thu Jul 21 00:00:00 EDT 2016}

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  • Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predictmore » that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy`s Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.« less
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