This　document　is　an　example　of　what　your　final　camera-ready　manuscript　to　APSIPA　ASC　2015　should　look　like.　Authors　are　asked　to　conform　to　the　directions　reported　in　this　document.　The　key　problem　in　Hidden　Markov　model　(HMM)-based　speech　enhancement　is　how　to　obtain　an　appropriate　weighted　Wiener　filter　(WWF).　This　paper　presents　a　gain-adaptive　parallel　HMM　(PHMM)　for　speech　enhancement　based　on　Mel-frequency　spectral　(MFS)　features　and　linear　prediction　(LP)　coefficients　of　speech　and　noise,　and　a　WWF　modified　by　the　speech-presence　probability　(SPP)　is　developed.　MFS-HMM　(i.e.,　the　HMM　in　MFS　domain)　and　LP-HMM　(i.e.,　the　HMM　in　LP　domain)　constitute　the　proposed　PHMM,　which　is　obtained　by　the　parallel　training　method.　The　forward　probabilities　in　all　mixtures　of　states　for　noisy　speech　MFS-HMM　are　calculated　as　the　weighting　factors　of　WWF.　In　order　to　obtain　more　accurate　noisy　speech　MFS-HMM　and　solve　the　mismatching　problem　of　spectral　energy　between　the　training　and　test　signals　in　MFS　domain,　we　introduce　two　gain　factors　to　adaptively　adjust　the　spectral　energy　of　speech　and　noise,　respectively.　The　gain　factors　are　estimated　online　by　the　expectation　maximization　(EM)　algorithm.　The　pre-trained　LP-HMMs　of　speech　and　noise　only　contain　the　spectral　shapes　of　speech　and　noise,　so　the　EM　algorithm　is　also　employed　to　estimate　LP　spectral　gains　for　constructing　Wiener　filters　in　the　proposed　WWF.　The　evaluation　results　confirm　the　superiority　of　the　proposed　method.　©　2015　Asia-Pacific　Signal　and　Information　Processing　Association.