Did you know the best compression methods out all have a variable length (measured in bits) alphabet? eg. Dynamic Markov Coding will start with just '0' and '1' and then predict the next bit but as it see's more symbols it will extend this to single characters (so see 'a' or 'b' and predict the next bit). They'll then continue as they learn more of the file and their alphabet will essentially include common pairwise letters, then words and entire common phrases.
This is actually a commonly missed aspect of Shannon entropy. A file of 0111101110111 repeated will give you a different result if you consider a 1 bit alphabet of 25% '0' and 75% '1' than a 4 bit alphabet of 100% '0111'. No one in the real world is using the character frequencies of english characters as a measure of Shannon entropy or Kolmogorov complexity. No algorithm expects that. They all work at the binary level and they will try to adjust the symbol lengths of the alphabet to common sequences to achieve the best result.
This is in fact the reason Kolmogorov complexity is used rather than Shannon entropy. Shannon entropy doesn't tell you how to define an optimal alphabet. That part is actually undefinable. It just tells you what to do if you have that already. Kolmogorov complexity says more completely 'find the optimal alphabet and the symbol probabilities and make a minimal sequence from that'.
Different human languages don't figure into this at all and are completely irrelevant.