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Content Provider | IEEE Xplore Digital Library |
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Author | Hank Liao |
Copyright Year | 2013 |
Description | Author affiliation: Google Inc., New York, NY, USA (Hank Liao) |
Abstract | There has been little work on examining how deep neural networks may be adapted to speakers for improved speech recognition accuracy. Past work has examined using a discriminatively trained affine transformation of the input features applied at a frame level or the re-training of the entire shallow network for a specific speaker. This work explores how deep neural networks may be adapted to speakers by re-training the input layer, the output layer or the entire network. We look at how L2 regularization using weight decay to the speaker independent model improves generalization. Other training factors are examined including the role momentum plays and stochastic mini-batch versus batch training. While improvements are significant for smaller networks, the largest show little gain from adaptation on a large vocabulary mobile speech recognition task. |
Starting Page | 7947 |
Ending Page | 7951 |
File Size | 356622 |
Page Count | 5 |
File Format | |
ISBN | 9781479903566 |
ISSN | 15206149 |
DOI | 10.1109/ICASSP.2013.6639212 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2013-05-26 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Hidden Markov models Adaptation models Acoustics Neural networks Training Speech Speech recognition Speaker adaptation Large vocabulary continuous speech recognition Multilayer perceptrons Deep neural networks |
Content Type | Text |
Resource Type | Article |
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