Emotion recognition is a technology that allows computers to recognize and understand human emotions. It plays an important role in many fields and is an important development direction in the field of artificial intelligence. Therefore， the research status of bimodal emotion recognition based on speech and text was summarized. Firstly， the representation space of emotion was classified and elaborated. Secondly， the emotion databases were classified according to their emotion representation space， and the common multi-modal emotion databases were summed up. Thirdly， the methods of bimodal emotion recognition based on speech and text were introduced， including feature extraction， modal fusion， and decision classification. Specifically， the modal fusion methods were highlighted and divided into four categories， namely feature level fusion， decision level fusion， model level fusion and multi-level fusion. In addition， results of a series of bimodal emotion recognition methods based on speech and text were compared and analyzed. Finally， the application scenarios， challenges， and future development directions of emotion recognition were introduced. The above aims to analyze and review the work of multi-modal emotion recognition， especially bimodal emotion recognition based on speech and text， providing valuable information for emotion recognition.