This　paper　addresses　the　application　in　speech　recognition　of　simulated　telephone　speech,　which　is　generated　from　clean　speech　by　approximately　mimicking　actual　telephone　channel　conditions.　Maximum　Likelihood　Linear　Regression　(MLLR)　algorithm　was　performed　to　conduct　experiments　on　evaluating　the　performances　of　HMM　recognizers,　which　were　trained　from　clean　speech　and　from　generated　telephone　data　respectively.　The　test　and　adaptation　data　were　recorded　by　piping　clean　speech　through　local　telephone　network.　The　experiments　without　adaptation　report　that　the　simulation　models　trained　on　generated　data　can　give　an　obviously　higher　rate　than　the　clean　speech.　The　adaptation　performances　show　that　MLLR　lends　itself　to　further　improving　the　recognition　performance　of　telephone　recognition　system.　The　results　show　that　telephone　speech　recognition　performance　can　be　effectively　improved　using　the　generated　data,　and　its　generating　method　can　reduce　the　acoustic　mismatch　between　training　and　testing　data　that　was　induced　by　the　shortage　of　actual　telephone　speech.