This　paper　proposed　a　data-driven　speech　enhancement　method　based　on　the　modeled　long-range　temporal　dynamics　(LRTDs).　First,　by　extracting　the　Mel-Frequency　Cepstral　coefficient　(MFCC)　features　from　speech　and　noise　corpora,　the　Gaussian　Mixture　Models　(GMMs)　of　the　speech　and　noise　were　trained　respectively　based　on　the　expectation-maximization　(EM)　algorithm.　Then,　the　LRTDs　were　obtained　from　the　GMM　models.　Next,　based　on　the　LRTDs,　a　modified　maximum　a　posterior　(MAP)　based　adaptive　longest　matching　segment　searching　(ALMSS)　method　derived　from　A*　search　technique　was　combined　with　the　Vector　Taylor　Series　(VTS)　approximation　algorithm　in　order　to　search　the　longest　matching　speech　and　noise　segments　(LMSNS)　from　speech　and　noise　corpora.　Finally,　using　the　obtained　LMSNS,　the　estimation　of　speech　spectrum　was　achieved.　Furthermore,　a　modified　Wiener　filter　was　constructed　to　further　eliminate　residual　noise.　The　objective　and　subjective　test　results　show　that　the　proposed　method　outperforms　the　reference　methods.　(C)　2017　Elsevier　B.V.　All　rights　reserved.