AUTOMATIC MODEL COMPLEXITY CONTROL USING MARGINALIZED DISCRIMINATIVE GROWTH FUNCTIONS
X. Liu & M. J. F. Gales
Designing a large vocabulary speech recognition system is a highly complex problem. Many techniques affect both the system complexity and recognition performance. Automatic complexity control criteria are needed to quickly predict the recognition performance ranking of systems with varying complexity, in order to select an optimal model structure with the minimum word error. In this paper a novel complexity control technique is proposed by using the marginalization of discriminative growth functions. A two stage approach is adopted to make the marginalization efficient. First a lower bound, related to the auxiliary function, is used to remove the dependence on the latent variables. Second a Laplace approximation is used for the integration. Experimental results on a spontaneous speech recognition task show that marginalized MMI growth function outperforms using held out data likelihood and standard Bayesian schemes in terms of both recognition performance ranking error and word error.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.