A dynamic semantic model for re-scoring recognition hypotheses

This paper describes the use of Belief Networks (BNs) for dynamic semantic modeling within the United Airlines’ FLight InFOrmation service (FLIFO). Callers can speak naturally to obtain status information about all flights (including arrival and departure times) of United Airlines. In this work we aim at enabling the application to utilize dynamic call information to improve speech recognition performance. Dynamic call information include the location of the caller, the time and date of the call, and the caller's dialog history. Dynamic semantic models can incorporate such additional information about the call in rescoring the N-best recognition hypotheses. Our experiments showed that this improved the recognition accuracy of flight number utterances from 84.95% to 86.80%.