The following packages are required to install Pocket Sphinx and its Python bindings:

The packages can be installed using the apt-get command. The following commands are used to install Pocket Sphinx and its Python interface.

Installing Pocket Sphinx in Ubuntu can be done either through source code or by package managers. Here, we will install Pocket Sphinx using the package manager:

Once we are done with the installation, we can work with Python scripting for speech recognition.

The following is the code to perform speech recognition using Pocket Sphinx and Python. The following code demonstrates how we can decode the speech recognition from a wave file:

#!/usr/bin/env python
import sys

#In Ubuntu 14.04.2, the pocketsphinx module shows error in first import and will work for the second import. The following code is a temporary fix to handle that issue
try:
  import pocketsphinx

except:
  import pocketsphinx

The preceding code will import the pocketsphinx Python module and Python sys module. The sys module contain functions that can be called during program runtime. In this code, we will use the sys module to get the wave filename from the command-line argument:

if __name__ == "__main__":
   hmdir = "/usr/share/pocketsphinx/model/hmm/en_US/hub4wsj_sc_8k"
   lmdir = "/usr/share/pocketsphinx/model/lm/en_US/hub4.5000.DMP"
   dictd = "/usr/share/pocketsphinx/model/lm/en_US/cmu07a.dic"

The hmdir, lmdirn, and dictd variables hold the path of HMM, LM (Language Model), and dictionary of Pocket Sphinx:

  #Receiving wave file name from command line argument
  wavfile = sys.argv[1]

The following code will pass HMM, LM, and the dictionary path of Pocket Sphinx to Pocket Sphinx's Decoder class. Read and decode the wave file. In the end, it will print the detected text:

   speechRec = pocketsphinx.Decoder(hmm = hmdir, lm = lmdir, dict = dictd)
   wavFile = file(wavfile,'rb')
   speechRec.decode_raw(wavFile)
   result = speechRec.get_hyp()

   print "


Detected text:>",result
   print "


"

The preceding code can be run using the following command:

$ python <code_name.py> <wave_file_name.wav>

The following is a screenshot of the output. The detected text was not the content on the wave file. The detection accuracy with the default acoustic model and LM is low; we have to train a new model or adapt an existing model to improve accuracy:

The previous method we discussed was an offline recognition; in the next section, we will see how to perform real-time speech recognition using Pocket Sphinx, GStreamer, and Python. In this approach, real-time speech data comes through the GStreamer framework and is decoded using Pocket Sphinx. To work with the GStreamer Pocket Sphinx interface, install the following packages:

The following command will install the GStreamer plugin for Pocket Sphinx:

$ sudo apt-get install gstreamer0.10-pocketsphinx

The following package will install the GStreamer Python binding. It will enable you to use GStreamer APIs from Python:

$ sudo apt-get install python-gst0.10

The following package will install the GStreamer plugin to get information from GConf:

$ sudo apt-get install gstreamer0.10-gconf