We report on two applications of Maximum Entropy-based ranking models to problems
of relevance to automatic speech recognition and text-to-speech synthesis. The
first is stress prediction in Russian, a language with notoriously complex
morphology and stress rules. The second is the classification of alphabetic
non-standard words, which may be read as words (NATO), as letter sequences (USA),
or as a mixed (mymsn). For this second task we report results on English, and five
other European languages.