The rating software is based on voice recognition with custom audio models and vocabulary, generated using built-in scripts (along with secondary rating modules based on silence and length recognition).
The call software is a very simple OOP wrapper around libtgvoip, making use of libopusenc (must be compiled by calling `make`) and libopusfile to encode and decode OGG OPUS files.

The submitted tgvoiprate app got a score of 551 and took the 2nd place in the scoring table, receiving 4⭐️.
Minor issue #issue8875 and external dependencies of pocketsphinx voice recognition (free open source) -0.2⭐️
Final result is 3.8⭐️

@Classy Bee, thanks for the explanation. You are right, because it is true that "the speech is still very understandable even with distortion". Please find attached files [1] used for comparison above. So in this case the high score, produced by your different approach (based on PocketSphinx speech recognition engine), makes sense. However we can see that for the same input ViSQOL-based and PESQ-based algorithms produce much lower score. Which makes even more interesting the question of how to select the best metric/algorithm for speech audio quality comparison in VoIP systems.
[1] http://s000.tinyupload.com/?file_id=88324864463913678152
P.S. Cannot attach .ogg / .zip / .tar.gz here.

a little bug: dependencies in deb-packages.txt must be one per line

And much more significant issue: your tgvoipcall depend on libopusenc.so wich you are not add to deb-packages.txt and i don't found it in Debian repo by apt search libopusenc :( I can't run your file

For a distorted ogg tgvoiprate returns 4.66 while others tgvoiprate return 1.98 (ViSQOL-based) and 1.95 (PESQ-based).

make fails with following error:

cd libopusenc && autoreconf -vfi && mkdir -p build && cd build && ../configure --prefix=/usr && make -j && sudo make install && cd ../..

autoreconf: 'configure.ac' or 'configure.in' is required

make: *** [Makefile:71: /usr/include/opus/opusenc.h] Error 1


and src/libopusenc folder is empty.

tgvoiprate returns values 0.0-1.0 for some input, while in the task "the rating should be expressed as a real number from 1.0 to 5.0". How should the output be adjusted from 0.0-5.0 to 1.0-5.0?