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Regularized Subspace Gaussian Mixture Models for Speech Recognition
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DOI：10.1109/LSP.2011.2157820.png)
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Abstract
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Subspace Gaussian mixture models (SGMMs) provide a compact representation of the Gaussian parameters in an acoustic model, but may still suffer from over-fitting with insufficient training data. In this letter, the SGMM state parameters are estimated using a penalized maximum-likelihood objective, based on l(1) and l(2) regularization, as well as their combination, referred to as the elastic net, for robust model estimation. Experiments on the 5000-word Wall Street Journal transcription task show word error rate reduction and improved model robustness with regularization.
Keywords:
l(1)/l(2)-norm penalty
elastic net
regularization
sparsity
subspace Gaussian mixture models
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