Training Context-Dependent Distributions
The polyphone collection process can easily produce hundreds of thousands, even millions of polyphones. It is obviously not feasible to use a fully continuous HMM model for each of them. Eventually we will want to use continuous density HMMs but for a smaller number of models. It is, however, possible to train hundreds of thousands of mixture weights distributions, such that they can be clustered into fewer afterwards. This is what we are going to do in this step. The complete script can be found as usual in the scripts thread.
The startup looks a bit different from the starups that we had so far, because we now have to incorporate the ptrees:
[FeatureSet fs] setDesc @/home/islpra0/IslData/featDesc fs setAccess @/home/islpra0/IslData/featAccess [CodebookSet cbs fs] read ../step8/codebookSet [DistribSet dss cbs] read ../step10/distribSet [PhonesSet ps] read ../step2/phonesSet [Tags tags] read ../step2/tags Tree dst ps:PHONES ps tags dss dst.ptreeSet read ../step10/ptreeSet dst read ../step10/distribTree SenoneSet sns [DistribStream str dss dst] [TmSet tms] read ../step2/transitionModels [TopoSet tps sns tms] read ../step2/topologies [Tree tpt ps:PHONES ps tags tps] read ../step2/topologyTree [DBase db] open ../step1/db.dat ../step1/db.idx -mode r [Dictionary diction ps:PHONES tags] read ../step4/convertedDict fs FMatrix LDAMatrixWe now load the last codebook weights. Remember that we don't have any distribution weights yet. We could load the context independent distribution weights and initialize every context dependent distribution with its corresponding context-independent distribution, but experiments have shown that this is not necessary. It is fine to not load any distribution weights and thus start with equally distributed values (i.e. every distribution value will be 1/16). Besides, the only reason why we are training these distributions is to cluster them later into fewer which will have to be trained anew, anyway:
fs:LDAMatrix.data bload ../step5/ldaMatrix AModelSet amo tpt ROOT HMM hmm diction amo Path path
cbs load ../step9/codebookWeights.3 cbs createAccus dss createAccusWe use the same Tcl procedure "forcedAlignment" that we used last time when training along labels and use a "regular" training loop. Feel free to test the resulting system using this script.