Paper: Modeling Topic Coherence For Speech Recognition

ACL ID C96-2154
Title Modeling Topic Coherence For Speech Recognition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996

St, atist,ical langmtge models play a ma- jor role in current spee~(:h re.cognition sys- tems. Most of these models have ti)- cussed on relatively local interactions be- tween words. R(.'(:ently, however, |her('. have been sevcr;d attempts to incorpo- rate other knowlcdg(; source.s, in par- ticular long(x-range word (tet)(;nden(:ies, in order to improve. Sl)(.~ech r(;( We will 1)rcs(~.nt one. such m('.t;ho(l, which tries to autonmticatly utilize t)rolmri;ics of topic continuity. Whim a l)asc-linc. re.(x)gnil;ion sysl;em gencra, l;('.s a.[- ternativ(', hypothe.s(~s for a senl;enc( L we will ul~ilize the word prefercn(:(~s based on topic coherence to sele(:t tim b(;st hy~ pothesis. In our experiment, we achi(wed a 0.65% imI)rovenmn|; in the wor(1 ei- ror rat(', on top of t...