Lattice-based Viterbi decoding techniques for speech translation

We describe a cardinal-synchronous Viterbi decoder for statistical phrase-based machine translation which can operate on general ASR lattices (as opposed to confusion networks). The decoder implements constrained source reordering on the input lattice and makes use of an outbound distortion model to score the possible reorderings. The phrase table, representing the decoding search space, is encoded as a weighted finite state acceptor which is determined and minimized. At a high level, the search proceeds by performing simultaneous transitions in two pairs of automata: (input lattice, phrase table FSM) and (phrase table FSM, target language model). An alternative decoding strategy that we explore is to break the search into two independent subproblems: first, we perform monotone lattice decoding and find the best foreign path through the ASR lattice and then, we decode this path with reordering using standard sentence-based SMT. We report experimental results on several testsets of a large scale Arabic-to-English speech translation task in the context of the global autonomous language exploitation (or GALE) DARPA project. The results indicate that, for monotone search, lattice-based decoding outperforms 1-best decoding whereas for search with reordering, only the second decoding strategy was found to be superior to 1-best decoding. In both cases, the improvements hold only for shallow lattices.