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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rusheng Hu Xiaolong Li Yunxin Zhao |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Comput. Sci., Missouri Univ., Columbia, MO, USA (Rusheng Hu; Xiaolong Li; Yunxin Zhao) |
| Abstract | We present a greedy EM (GEM) method for training Gaussian mixture density (GMD) based acoustic models. In the proposed approach, starting from a single Gaussian, GMD is built up by sequentially adding new components. Each new component is globally selected to avoid local optima. The sequential procedure offers more control over the model structure to achieve better coverage of data. GEM also provides a natural way of integrating information criteria for model complexity selection. Experimental results on a WSJ task show that the new method performs consistently better than the conventional method in speech recognition word error rate. |
| Sponsorship | IEEE Signal Processing Soc |
| File Size | 229507 |
| File Format | |
| ISBN | 0780388747 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2005.1415209 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-03-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech recognition Error analysis Shape control Hidden Markov models Computer science Training data Statistics Optimization methods Performance evaluation |
| Content Type | Text |
| Resource Type | Article |
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