A speech emotion detection method, performed by a computing device, comprising: obtaining speech data (step 102); performing framing processing on the speech data to obtain speech frames (step 104); extracting speech features corresponding to the speech frames, to form a speech feature matrix corresponding to the speech data; inputting the speech feature matrix to a trained emotion state probability detection model, and outputting an emotion state probability matrix corresponding to the speech data (step 106); inputting the emotion state probability matrix and the speech feature matrix to a trained emotion state transition model to obtain an emotion state sequence corresponding to the speech data (step 108); and determining, according to the emotion state sequence, an emotion state corresponding to the speech data (step 110), wherein the emotion state probability comprises a silent probability; and the determining, based on the emotion state sequence, an emotion state corresponding to the speech data comprises: detecting a silent frame in the speech data based on a silent state comprised in the emotion state sequence, and segmenting the speech data based on the silent frame, to obtain non-silent speech sub-segments (step 110A); and determining, based on emotion state sequences corresponding to the non-silent speech sub-segments, emotion states corresponding to the non-silent speech sub-segments (step 110B), wherein: the segmenting of the speech data based on the silent frames comprises segmenting the speech data only when a plurality of successive silent frames is detected and exceeds a preset threshold. obtaining speech data (step 102); performing framing processing on the speech data to obtain speech frames (step 104); extracting speech features corresponding to the speech frames, to form a speech feature matrix corresponding to the speech data; inputting the speech feature matrix to a trained emotion state probability detection model, and outputting an emotion state probability matrix corresponding to the speech data (step 106); inputting the emotion state probability matrix and the speech feature matrix to a trained emotion state transition model to obtain an emotion state sequence corresponding to the speech data (step 108); and determining, according to the emotion state sequence, an emotion state corresponding to the speech data (step 110), wherein the emotion state probability comprises a silent probability; and the determining, based on the emotion state sequence, an emotion state corresponding to the speech data comprises: detecting a silent frame in the speech data based on a silent state comprised in the emotion state sequence, and segmenting the speech data based on the silent frame, to obtain non-silent speech sub-segments (step 110A); and determining, based on emotion state sequences corresponding to the non-silent speech sub-segments, emotion states corresponding to the non-silent speech sub-segments (step 110B), wherein: the segmenting of the speech data based on the silent frames comprises segmenting the speech data only when a plurality of successive silent frames is detected and exceeds a preset threshold. detecting a silent frame in the speech data based on a silent state comprised in the emotion state sequence, and segmenting the speech data based on the silent frame, to obtain non-silent speech sub-segments (step 110A); and determining, based on emotion state sequences corresponding to the non-silent speech sub-segments, emotion states corresponding to the non-silent speech sub-segments (step 110B), wherein: the segmenting of the speech data based on the silent frames comprises segmenting the speech data only when a plurality of successive silent frames is detected and exceeds a preset threshold.