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RECENT ADVANCES IN DEEP LEARNING FOR SPEECH RESEARCH AT MICROSOFT
| Citations: | 23 - 10 self |
BibTeX
@MISC{Deng_recentadvances,
author = {Li Deng and Jinyu Li and Jui-ting Huang and Kaisheng Yao and Dong Yu and Frank Seide and Michael L. Seltzer and Geoff Zweig and Xiaodong He and Jason Williams and Yifan Gong and Alex Acero},
title = {RECENT ADVANCES IN DEEP LEARNING FOR SPEECH RESEARCH AT MICROSOFT},
year = {}
}
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Abstract
Deep learning is becoming a mainstream technology for speech recognition at industrial scale. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. We organize this overview along the feature-domain and model-domain dimensions according to the conventional approach to analyzing speech systems. Selected experimental results, including speech recognition and related applications such as spoken dialogue and language modeling, are presented to demonstrate and analyze the strengths and weaknesses of the techniques described in the paper. Potential improvement of these techniques and future research directions are discussed. Index Terms — deep learning, neural network, multilingual, speech recognition, spectral features, convolution, dialogue
