Unsupervised re-scoring of observation probability based on maximum entropy criterion by using confidence measure with telephone speech

This paper describes a two-step Viterbi decoding based on reinforcement learning and information theory with telephone speech. The idea is to strength or weaken HMM’s by using Bayesbased 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. The current reinforcement learning mechanism is modeled as the linear combination of two metrics or information sources. Moreover, a criterion based on incremental conditional entropy maximization to optimize a linear combination of metrics or information sources is also presented. As shown here, the method requires only one adapting utterance and can lead to a reduction in WER as high as 10.9%.