Hi everyone,
as the title states I have a problem with pocketsphinx in python using the
pocketsphinx GStreamer plugin.
Using pocketsphinx_batch with default lm, hmm and dictionary (by not
specifying any) I achieve a WER of
about 64%. Doing the same with the plugin I get no hypotheses and therefore an
errorrate of 100%.
If I specify an lm, hmm and dictionary I do get results but they are still
much worse in performance than
with pocketsphinx_batch.
I am building my gstreamer pipeline from a string which looks like this:
The hypothesis is then caught on a signal just as in this tutorial: http://cm
usphinx.sourceforge.net/wiki/gstreamer
Does anyone know what could cause this behavior?
Please let me know if you need more information in order to help.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Plugin default configuration is slightly different from the
pocketsphinx_continuous default configuration. The differences are:
Plugin:
-maxhmmpf 1000
-maxwpf 10
-fwdflat no
-bestpath no
Pocketsphinx_continuous:
-bestpath yes
-fwdflat yes
-maxhmmpf not set
-maxwpf not set
The differences are made to provide the best decoding speed in gstreamer
plugin. If you syncronize the parameters accuracy and results should be the
same.
You still need a better language model because WER more than 60% is senseless
to compare. 60% or 100% it doesn't really matter, it just mean your decoder is
broken.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Well, using another dataset for which I have an optimized language model I
achie a WER of 50%. The main problem is that the plugin doesn't recognize
anything at all. The hypothesis is just an empty string.
With the changed properties in the plugin I do get results but they still
differ from the pocketsphinx_batch output. The results are also differing if I
reverse this and use the values of the plugin for pocketsphinx_batch I don't
get the plugins results either. :(
I have absolutely no clue why I can't get the same results.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hi everyone,
as the title states I have a problem with pocketsphinx in python using the
pocketsphinx GStreamer plugin.
Using pocketsphinx_batch with default lm, hmm and dictionary (by not
specifying any) I achieve a WER of
about 64%. Doing the same with the plugin I get no hypotheses and therefore an
errorrate of 100%.
If I specify an lm, hmm and dictionary I do get results but they are still
much worse in performance than
with pocketsphinx_batch.
I am building my gstreamer pipeline from a string which looks like this:
filesrc location=/path/to/file ! wavparse ! audioconvert !audioresample !
audio/x-raw-int, rate=8000, channels=1,
width=16,depth=16,endianness=1234,signed=true ! pocketsphinx fakesink
The hypothesis is then caught on a signal just as in this tutorial: http://cm
usphinx.sourceforge.net/wiki/gstreamer
Does anyone know what could cause this behavior?
Please let me know if you need more information in order to help.
Hello
Plugin default configuration is slightly different from the
pocketsphinx_continuous default configuration. The differences are:
Plugin:
Pocketsphinx_continuous:
The differences are made to provide the best decoding speed in gstreamer
plugin. If you syncronize the parameters accuracy and results should be the
same.
You still need a better language model because WER more than 60% is senseless
to compare. 60% or 100% it doesn't really matter, it just mean your decoder is
broken.
Well, using another dataset for which I have an optimized language model I
achie a WER of 50%. The main problem is that the plugin doesn't recognize
anything at all. The hypothesis is just an empty string.
With the changed properties in the plugin I do get results but they still
differ from the pocketsphinx_batch output. The results are also differing if I
reverse this and use the values of the plugin for pocketsphinx_batch I don't
get the plugins results either. :(
I have absolutely no clue why I can't get the same results.