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.
Bibliographic reference. Sproat, Richard / Hall, Keith (2014): "Applications of maximum entropy rankers to problems in spoken language processing", In INTERSPEECH-2014, 761-764.