The LIMSI 1999 Hub-4E Transcription System

In this paper we report on the LIMSI 1999 Hub-4E system for broadcast news transcription. The main difference from our previous broadcast news transcription system is that a new decoder was implemented to meet the 10xRT requirement. This single pass 4-gram dynamic network decoder is based on a time-synchronous Viterbi search with dynamic expansion of LM-state conditioned lexical trees, and with acoustic and language model lookaheads. The decoder can handle position-dependent, cross-word triphones and lexicons with contextual pronunciations. Faster than real-time decoding can be obtained using this decoder with a word error under 30%, running in less than 100 Mb of memory on widely available platforms such Pentium III or Alpha machines. The same basic models (lexicon, acoustic models, language models) and partitioning procedure used in past systems have been used for this evaluation. The acoustic models were trained on 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 language model interpolated with a category trigram model. The overall word transcription error on the 1999 evaluation test data was 17.1% for the baseline 10X