1. First compile the Julian grammar files (i.e. the one you added to you test directory in Step 2) as follows:
$mkdfa.pl sample
2. Next, create a file in your test directory to tell Julius where
all your wav files are
located (Julius can use the same mfc files as does HTK, but recognition
rates seem better when using the original wav files). It should
look like this:
wavlst
3. Next update your Julian configuration file as follows:
5. Next you need process the Julius output so that HTK's HResults
command can read it. Download the following script:
ProcessJuliusOutput.pl
(note that if you download this file,
you need to rename it to 'processjuliusoutput.pl' - otherwise it will
download
as 'processjuliusoutput_pl.txt').
6. Then execute it as follows (note: you may need to make this script executable - see Cheat Sheet on the Docs page):
7. Finally, run the following command to determine the actual recognition performance of the Acoustic Model:
$HResults -I testref.mlf tiedlist juliusProcessed
which will display output similar to this (note: these are results
for the 8kHz:16-bit VoxForge Speaker Independent Acoustic Model, build
396, which includes speech audio using my voice, so the results are better than what you would get):
for the line starting with SENT, there were 50 test sentences and 82% were correctly recognized.
for the line starting with WORD, there were 189 words in total,
of which 96.83%
were recognized correctly. But because Julius recognized words
that are not in the audio file (i.e. insertion errors) it only gets a 94.71% accuracy rating.
Count definitions:
D - Deletion Error
S - Substitution Error
I - Insertion Error
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wavlst
ProcessJuliusOutput.pl
(note that if you download this file,
you need to rename it to 'processjuliusoutput.pl' - otherwise it will
download
as 'processjuliusoutput_pl.txt').