Signal　pre-processing　and　post-processing　are　becoming　two　key　factors　that　impact　embedded　speech　recognition　systems　from　the　laboratory　to　practical　application.　Speech　endpoint　detection　and　out-of-vocabulary　rejection　are　the　most　important　part　of　the　speech　pre-processing　and　post-processing　respectively.　The　performance　of　traditional　speech　endpoint　detection　based　on　short-term　energy　and　zero-crossing　rate　degrade　dramatically　in　noisy　environments.　Methods　based　on　frequency-domain　need　complex　computing,　and　they　can　not　meet　embedded　systems　well.　In　this　paper,　we　present　a　new　endpoint　detection　algorithm　that　is　based　on　statistical　theory　for　isolated-word.　The　correct　endpoint　detection　rate　reaches　97.40%　using　the　method.　In　this　paper　one-class　support　vector　machine　theory　is　introduced　to　solve　out-of-vocabulary　rejection.　Using　this　algorithm　system,　true　recognition　fraction(TRF)　is　up　to　96%,　and　false　recognition　fraction(FRF　)　is　about　95%.　©　2010　IEEE.