Evaluation of Spoken Language Grammar Learning in ATIS Domain

  • Ye-Yi Wang ,
  • Alex Acero

IEEE International Conference on Acoustics, Speech, and Signal Processing |

Published by Institute of Electrical and Electronics Engineers, Inc.

To facilitate the development of speech enabled applications and services, researchers have been working on a variety of smart tools. Recently, we introduced a schema-based context free grammar learning algorithm aiming at the development of real applications. In that paper, we described the algorithm and gave some experimental results on the data of our in-house project. To study the general applicability of the algorithm as well as to provide the research community with more informative results, we apply the algorithm to the well studied ATIS (Airline Travel Information System) task and compare the performance of the learned grammar with one of the best performers in ATIS evaluations. The results show that the semi-automatically learned grammar achieves comparable performance to the manually authored grammar.