LAUSR

Speech emotion recognition (SER) is a non-trivial task for humans, while it remains challenging for automatic SER due to the linguistic complexity and contextual distortion. Notably, previous automatic SER systems always regarded multi-modal information and temporal relations of speech as two independent tasks, ignoring their association. We argue that the valid semantic features and temporal relations of speech are both meaningful event relationships. This paper proposes a novel temporal relation inference network (TRIN) to help tackle multi-modal SER, which fully considers the underlying hierarchy of phonetic structure and its associations between various modalities under the sequential temporal guidance. Mainly, we design a temporal reasoning calibration module to imitate real and abundant contextual conditions. Unlike the previous works, which assume all multiple modalities are related, it infers the dependency relationship between the semantic information from the temporal level and learns to handle the multi-modal interaction sequence with a flexible order. To enhance the feature representation, an innovative temporal attentive fusion unit is developed to magnify the details embedded in a single modality from semantic level. Meanwhile, it aggregates the feature representation from both the temporal and semantic levels to maximize the integrity of feature representation by an adaptive feature fusion mechanism to selectively collect the implicit complementary information to strengthen the dependencies between different information subspaces. Extensive experiments conducted on two benchmark datasets demonstrate the superiority of our TRIN method against some state-of-the-art SER methods.