Original language | English |
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Pages (from-to) | 1192-1199 |
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Number of pages | 8 |
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Journal | Speech Communication |
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Volume | 48 |
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Issue number | 9 |
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DOIs | |
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Publication status | Published - 1 Sep 2006 |
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This work introduces two methods for adapting the observation process parameters of linear dynamic models (LDM) or other linear-Gaussian models. The first method uses the expectation-maximization (EM) algorithm to estimate transforms for location and covariance parameters, and the second uses a generalized EM (GEM) approach which reduces computation in making updates from $O(p^6)$ to $O(p^3)$, where $p$ is the feature dimension. We present the results of speaker adaptation on TIMIT phone classification and recognition experiments with relative error reductions of up to $6. Importantly, we find minimal differences in the results from EM and GEM. We therefore propose that the GEM approach be applied to adaptation of hidden Markov models which use non-diagonal covariances. We provide the necessary update equations.
ID: 2076882