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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Foote, J.T. |
| Copyright Year | 1995 |
| Description | Author affiliation: Dept. of Eng., Cambridge Univ., UK (Foote, J.T.) |
| Abstract | This paper presents a method of non-parametrically modeling HMM output probabilities. Discrete output probabilities are estimated from a tree-based maximum mutual information (MMI) partition of the feature space, rather than the usual vector quantization. One advantage of a decision-tree method is that very high-dimensional spaces can be partitioned. Time variation can then be explicitly modeled by concatenating time-adjacent vectors, which is shown to improve recognition performance. Though the model is discrete, it provides recognition performance better than i-component Gaussian mixture HMMs on the ARPA Resource Management (RM) task. This method is not without drawbacks: because of its non-parametric nature, a large number of parameters are needed for a good model and the available RM training data is probably not sufficient. Besides the computational advantages of a discrete model, this method has promising applications in talker identification, adaptation, and clustering. |
| Starting Page | 461 |
| Ending Page | 464 |
| File Size | 553730 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780324315 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.1995.479628 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1995-05-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Speech recognition Mutual information Training data Decision trees Resource management Quantization Robustness Greedy algorithms Cost function |
| Content Type | Text |
| Resource Type | Article |
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