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Stochastic finite automata arise naturally in many language and speechprocessing tasks. They include stochastic acceptors, which represent certainprobability distributions over random strings. We consider the problem ofefficient sampling: drawing random string variates from the probabilitydistribution represented by stochastic automata and transformations of those.We show that path-sampling is effective and can be efficient if theepsilon-graph of a finite automaton is acyclic. We provide an algorithm thatensures this by conflating epsilon-cycles within strongly connected components.Sampling is also effective in the presence of non-injective transformations ofstrings. We illustrate this in the context of decoding for ConnectionistTemporal Classification (CTC), where the predictive probabilities yieldauxiliary sequences which are transformed into shorter labeling strings. We cansample efficiently from the transformed labeling distribution and use this intwo different strategies for finding the most probable CTC labeling.

Sampling from Stochastic Finite Automata with Applications to CTC Decoding