Modeling Topic Coherence for Speech Recognition

St, atisfical kmguage models play a jor role in eraTent speech recognition sys- tems. Most of fimse models have cussed on relatively local interactions between words. Recently, however, there have been several atempl;s to incort)o rate other knowlcdge sotn't:es in parLicular longerqmge wor(1 (let)enden(:ics, in order to improve sl)ee(:h recognizers. We will ln'csent one such mefio(l, whit:h trics 1o mtlmnatieatly utilize prolmrLics ()f topic continuity. When a bastMine st)ce(:h recognition system general,es tcrnative hypotheses fi)r a sentence, we will utilize the word prc[rences based on topic (:oherencc to sclc(:t the 5(s/, hp pothcsis. In (mr experiment, we achieved a 0.65% imI)rovcnmnt in the wor(1 for rate (mt, op of the base-line system. 1[ corresi)onds to 10.40% of the possible word error improvement