We introduce a novel efficient method, which improves the performance of speech recognition systems by providing the option to partially compile the word lattice into a deterministic finite-state automaton, making it suitable for the rescoring step in the speech recognition process. In contrast to the widely used n-best method our method permits the consideration of significantly larger number of alternatives within the same time-constraint and thus provides better recognition results. In this paper we present a description of the new method and empirical evaluation of its performance in comparison with the n-best method. The achieved WER reduction is up to 3.77 % at a p-value below 3 %. An important advantage of our method is its applicability for real-time applications.
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