Applications of Maximum Entropy Rankers to Problems in Spoken Language Processing
Abstract
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.
Research Areas
