This paper proposes a new paradigm to compensate for mismatch condition in speech recognition. A two-step Viterbi decoding based on reinforcement learning is described. The idea is to strength or weaken HMM's by using Bayes-based confidence measure ( BBCM) and distances between models. If HMM's in the N-best list show a low BBCM, the second Viterbi decoding will prioritize the search on neighboring models according to their distances to the N-best HMM's. As shown here, a reduction of 6% in WER is achieved in a task which results difficult for standard MAP and MLLR adaptation.
Bibliographic reference. Molina, Carlos / Yoma, Nestor Becerra / Huenupán, Fernando / Garreton, Claudio (2007): "Unsupervised re-scoring of observation probability in viterbi based on reinforcement learning by using confidence measure and HMM neighborhood", In INTERSPEECH-2007, 1733-1736.