Option 1 - Recognition

HDECODE can be used to generate 1-best output, or lattices. For both options the same configuration file, assumed to be stored in config.hdecode, may be used. This should contain the following entries

TARGETKIND     = MFCC_0_D_A   
HLANGMODFILTER = 'gunzip -c $.gz'
HNETFILTER     = 'gunzip -c $.gz'
HNETOFILTER    = 'gzip -c > $.gz'
RAWMITFORMAT   = T
STARTWORD      = SENT-START
ENDWORD        = SENT-END

This configuration file has specified the frontend, the filter for reading the language model^3.9, HLANGMODFILTER and filters for reading and writing lattices, HNETFILTER and HNETOFILTER respectively.

Recognition can then be run on the files specified in test.scp using the following command.

    HDecode -H hmm20/models -S test.scp \
          -t 220.0 220.0 \
          -C config.hdecode -i recout.mlf -w bg_lm \
          -p 0.0 -s 5.0 dict.hdecode xwrdtiedlist

The output will be written to an MLF in recout.mlf. The -w option specifies the $n$-gram model, in this case a bigram to be used. The final recognition results may be analysed using HRESULTS in the same way as HVITE.

In common with HVITE, there are a number of options that need to be set empirically to obtain good recognition performance and speed. The options -p and -s specify the word insertion penalty and the grammar scale factor respectively as in HVITE. There are also a number of pruning options that may be tuned to adjust the run time. These include the main beam (see the -t option), word end beam (see the -v option) and the maximum model pruning (see the -u option).