In　this　paper,　a　data-driven　speech　enhancement　method　based　on　modeled　long-range　temporal　dynamics　(LRTDs)　is　proposed.　First,　given　speech　and　noise　corpora,　Gaussian　Mixture　Models　(GMMs)　of　the　speech　and　noise　can　be　trained　respectively　based　on　the　expectation-maximization　(EM)　algorithm.　Then,　the　LRTDs　are　obtained　from　the　GMM　models.　Next,　based　on　the　LRTDs,　a　noise　robustness　longest　segment　searching　(NRLSS)　method　combined　with　the　Vector　Taylor　Series　(VTS)　approximation　algorithm　is　adopted　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　is　achieved.　Furthermore,　a　modified　Wiener　filter　is　constructed　to　further　eliminate　residual　noise.　The　test　results　show　that　the　proposed　method　outperforms　the　state-of-the-art　speech　enhancement　methods.