The LIMSI 1997 Hub-4E Transcription System

In this paper we report on the LIMSI system used in the Nov’97 Hub-4E benchmark test on transcription of American English broadcast news shows. There are two main differences from the LIMSI system developed for the Nov’96 evaluation. The first concerns the preprocessing stages for partitioning the data, and the second concerns a reduction in the number of acoustic model sets used to deal with the various acoustic signal characteristics. The LIMSI system for the November 1997 Hub-4E evaluation is a continuous mixture density, tied-state cross-word contextdependent HMM system. The acoustic models were trained on the 1995 and 1996 official Hub-4E training data containing about 80 hours of transcribed speech material. The 65K word trigram language models are trained on 155 million words of newspaper texts and 132 million words of broadcast news transcriptions. The test data is segmented and labeled using Gaussian mixture models, and non-speech segments are rejected. The speech segments are classified as telephone or wide-band, and according to gender. Decoding is carried out in three passes, with a final pass incorporating cluster-based test-set MLLR adaptation. The overall word transcription error of the Nov’97 unpartitioned evaluation test data