Multimodal　sentiment　analysis　is　an　actively　growing　research　area　that　utilizes　language,　acoustic　and　visual　signals　to　predict　sentiment　inclination.　Compared　to　language,　acoustic　and　visual　features　carry　a　more　evident　personal　style　which　may　degrade　the　model　generalization　capability.　The　issue　will　be　exacerbated　in　a　speaker-independent　setting,　where　the　model　will　encounter　samples　from　unseen　speakers　during　the　testing　stage.　To　mitigate　personal　style＇s　impact,　we　propose　a　framework　named　SIMR　for　learning　speaker-independent　multimodal　representation.　This　framework　separates　the　nonverbal　inputs　into　style　encoding　and　content　representation　with　the　aid　of　informative　cross-modal　correlations.　Besides,　in　terms　of　integrating　cross-modal　complementary　information,　the　classical　transformer-based　approaches　are　inherently　inclined　to　discover　compatible　cross-modal　interactions　but　ignore　incompatible　ones.　In　contrast,　we　suggest　simultaneously　locating　both　through　an　enhanced　cross-modal　transformer　module.　Experimental　results　show　that　the　proposed　model　achieves　state-of-the-art　performance　on　several　datasets.