2, the value of distortion score peaks at d=1, i.e., the monotonic alignment, and decays for non-monotonic alignments depending on how far it diverges from the monotonic alignment.
following Och and Ney (2003), we use a fixed value p 0 for the probability of jumping to a null state, which can be optimized on held-out data, and the overall distortion model becomes 0 0 if state ( | , ) (1 ) ( | , ) otherwise Given an HMM, the Viterbi alignment algorithm can be applied to find the best alignment between the backbone and the hypothesis,   the alignment produced by the algorithm cannot be used directly to build a confusion network.