8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Unsupervised Re-Scoring of Observation Probability in Viterbi Based on Reinforcement Learning by Using Confidence Measure and HMM Neighborhood

Carlos Molina, Nestor Becerra Yoma, Fernando Huenupán, Claudio Garreton

Universidad de Chile, Chile

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.

Full Paper

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.