http://cmusphinx.sourceforge.net/2012/08/gsoc-2012-pronunciation-evaluation-using-cmusphinx-%E2%80%93-project-conclusions/
http://cmusphinx.svn.sourceforge.net/viewvc/cmusphinx/branches/speecheval/ronanki/

But the Scoring results from the python project seems more accurate and correct. Can I use this python project with OpenEar in iOS application.

Thanks!

Sorry, I don’t know which algorithm they used and whether it’s the same (or, without a direct comparison of identical data and information on what the actual results are to compare it with, if there is a difference in scoring accuracy). There is no Python support in iOS, so I think your option would only be to port it to C/C++/Objective-C if you discover that it is a better algorithm.

Python code works as below for pronunciation score. I just want to know your scoring routines works like below (no need to be exactly the same). This way I will not required to port all python code (or even a single line). It will save a lot of my time.

Python Scoring Routines Text Dependent:
This method is based on exemplars for each phrase. Initially, mean acoustic score, mean duration along with deviations are calculated for each of the phone in the phrase based on exemplar recordings (acoustic scores for each phoneme from audio file can be calculated using pocketsphinx). Now, given the test recording, each phone in the phrase is then compared with exemplar statistics. After that, z-scores are calculated and then normalized scores are calculated based on maximum and minimum of z-scores from exemplar recordings. All phone scores are aggregated to get word score and then all word scores are aggregated with POS weight to get complete phrase score.

You can find more details here:
http://goo.gl/cvaAH

Regards,

Regards,