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Regularized Subspace Gaussian Mixture Models for Speech Recognition

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3 Author(s)
Liang Lu ; Univ. of Edinburgh, Edinburgh, UK ; Ghoshal, A. ; Renals, S.

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 l1 and l2 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.

Published in:

Signal Processing Letters, IEEE  (Volume:18 ,  Issue: 7 )