Making　virtual　human＇s　actions　more　natural　and　more　approaching　to　reality　is　helpful　for　enhancing　intelligibility　of　synthesized　Chinese　sign　language　(CSL).　Usually　action　with　prosody　looks　more　vivid　and　prosody　means　that　same　word　has　discriminating　style　according　to　different　context.　The　problem　here　is　how　to　find　and　indicate　the　context.　As　formal　description　for　CSL,　CSLML　provides　a　set　of　top　level　tags　for　recording　context.　This　paper　provides　a　new　method　for　finding　prosody　information　of　context,　which　is　utilizing　speech　sounds.　Hidden　Markov　model　(HMM)　is　trained　for　finding　different　prosody　patterns,　such　as　word　stressing,　sentence　modal,　and　emotions　etc.　And　parameters　of　HMM　are　mapped　to　value　of　tags　in　CSLML.　The　prosody　tags　are　then　transferred　to　animation　parameter　to　synthesize　prosodic　Chinese　Signed　Language　expression.　Experiments　in　this　paper　show　that　synthesized　animation　driven　by　prosody　parameters　extracted　with　HMM　is　more　realistic.　©　2013　IEEE.