Multimodal　Emotion　Recognition　in　Conversation　(MER)　aims　to　identify　emotions　in　conversation　by　integrating　text,　audio,　and　visual　information,　playing　a　crucial　role　in　dialogue　system,　recommendation　system　and　healthcare.　Existing　methods　overlook　the　contribution　discrepancy　among　modalities　when　integrating　multimodal　information,　leading　to　the　problem　of　imbalanced　optimization,　which　in　turn　results　in　suboptimal　performance.　To　address　this　problem,　we　propose　a　Debiased　Hierarchical　Knowledge　Distillation　(DHKD)　framework　that　enhances　weak　modalities’　contribution　and　mitigates　contribution　discrepancy　through　innovative　knowledge　distillation,　achieving　a　balanced　optimization.　As　the　major　contribution　in　our　proposed　model,　the　debiased　hierarchical　knowledge　distillation　is　designed　to　transfer　the　knowledge　from　the　multimodality　to　the　others　at　both　the　feature　and　logit　levels.　It　boosts　the　contribution　of　single　modalities,　especially　for　these　modalities　with　weak　contribution.　Extensive　experiments　on　IEMOCAP　and　MELD　datasets　demonstrate　the　effectiveness　of　our　approach.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.