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Posterior probability decoding, confidence estimation and system combination

In this paper the estimation of word posterior probabilities is discussed and their application in the CU-HTK system used in the March 2000 Hub5 Conversational Telephone Speech evaluation is described. The word lattices produced by the Viterbi decoder were used to generate confusion networks, which provide a compact representation of the most likely word hypotheses and their associated word posterior probabilities. These confusion networks were used in a number of post-processing steps. The 1-best sentence hypotheses extracted directly from the networks are shown to be significantly more accurate than the baseline decoding results. The posterior probability estimates were used as the basis for the estimation of word-level confidence scores. A new system combination technique is presented that uses these confidence scores and the confusion networks and performs better than the well-known ROVER technique.