Joining advantages of word-conditioned and token-passing decoding

We compare the families of token-passing and word conditioned decoders, and derive a more efficient dynamic decoder. The advantage of the word conditioned approach is a trivially simple hypothesis recombination, while the advantage of the token-passing approach is a straight-forward minimization of the search network with compressed word tails. We derive a dynamic decoder which joins the advantages of both decoding architectures by minimizing the search network of a word conditioned decoder. We describe the decoder and analyze its efficiency regarding acoustic look-ahead, network minimization, and facilitated pruning methods. Finally, we compare the new decoder with a WFST based decoder extended by acoustic look-ahead.