We describe our work on automatic transcription of radio and television news broadcasts. This problem is very challenging for large vocabulary speech recognition because of the frequent and unpredictable changes that occur in speaker, speaking style, topic, channel, and background conditions. Faced with such a problem, there is a strong tendency to try to carve the input into separable classes and deal with each one independently. In our early work on this problem, however, we are finding that the rewards for condition-specific techniques are disappointingly small. This is forcing us to look for general, robust, and adaptive algorithms for dealing with extremely variable data. We describe the BBN BYB-LOS recognition system configured to handle off-line transcription and we characterize the speech contained in the 1996 DARPA Hub-4 testbed. On the partitioned development test set, we achieved a 29.4% overall word error rate.
['Francis Kubala', 'Hubert Jin', 'Spyros Matsoukas', 'Long Nguyen', 'Richard M. Schwartz']
Broadcast news transcription