Abstract

In this paper we report on our Nov98 Hub-4E system, which is an extension of our Nov97 system[4]. The LIMSI system for the November 1998 Hub-4E evaluation is a continuous mixture density, tied-state cross-word context-dependent HMM system. The acoustic models were trained on the 1995, 1996 and 1997 official Hub-4E training data containing about 150 hours of transcribed speech material. 65K word language models were obtained by interpolation of backoff n-gram language models trained on different text data sets. Prior to word decoding a maximum likelihood partitioning algorithm segments the data into homogenous regions and assigns gender, bandwidth and cluster labels to the speech segments. Word decoding is carried out in three steps, integrating cluster-based MLLR acoustic model adaptation. The final decoding step uses a 4-gram languagemodel interpolated with a category trigram model. The main differences compared to last year's system arise from the use of additional acoustic and lang...

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