Automatic Transcription of English Broadcast News

In this paper the Philips Broadcast News transcription system is described. The Broadcast News task aims at the recognition of \found" speech in radio and television broadcasts without any additional side information (e.g. speaking style, background conditions). The system was derived from the Philips continuous mixture density crossword HMM system, using MFCC features and Laplacian densities. A segmentation was performed to obtain sentence-like partitions of the broadcasts. Using data-driven clustering, the obtained segments were grouped into clusters with similar acoustic conditions for adaptation purposes. Gender independent wordinternal and crossword triphone models were trained on 70 hours of the HUB4 training data. No focus condition speci c training was applied. Channel and speaker normalization was done by mean and variance normalization as well as VTN and MLLR. The transcription was produced by an adaptive multiple pass decoder starting with phrase-bigram decoding using word-internal triphones and nishing with a phrasetrigram decoding using MLLR-adapted crossword models.