Improved statistical models for SMT-based speaking style transformation

Automatic speech recognition (ASR) results contain not only ASR errors, but also disfluencies and colloquial expressions that must be corrected to create readable transcripts. We take the approach of statistical machine translation (SMT) to “translate” from ASR results into transcript-style text. We introduce two novel modeling techniques in this framework: a context-dependent translation model, which allows for usage of context to accurately model translation probabilities, and log-linear interpolation of conditional and joint probabilities, which allows for frequently observed translation patterns to be given higher priority. The system is implemented using weighted finite state transducers (WFST). On an evaluation using ASR results and manual transcripts of meetings of the Japanese Diet (national congress), the proposed methods showed a significant increase in accuracy over traditional modeling techniques.