Dependency modeling with bayesian networks in a voicemail transcription system
In this paperwe applyBayesiannetworksto the problem of voicemail transcription. We usea Bayesiannetwork systemto testa varietyof probabilisticmodelsthatmodel acousticcontext in additionto phoneticstateandacoustic observations.Weusea context variablethathastheability to modelcontextualphenomenathatarenot impliedby the linguistic sequenceof phones(e.g. noiselevel or speech rate). In rescoringexperiments,we areableto geta slight gain over a morestandardsystemwith a similar number of parameters.We obtainedthebestperformanceby conditioning the mixture coefficientson context, thus implementinga state-wisetied mixturesystem.In anutteranceclusteringsystem,analysisof the learnedparametersindicatesthatthecontext variableis highly correlatedwith and .