Vector learning representation for generalized speech emotion recognition.

ABSTRACT: Speech emotion recognition (SER) plays an important role in global business today to improve service efficiency. In the literature of SER, many techniques have been using deep learning to extract and learn features. Recently, we have proposed end-to-end learning for a deep residual local feature learning block (DeepResLFLB). The advantages of end-to-end learning are low engineering effort and less hyperparameter tuning. Nevertheless, this learning method is easily to fall into an overfitting problem. Therefore, this paper described the concept of the "verify-to-classify" framework to apply for learning vectors, extracted from feature spaces of emotional information. This framework consists of two important portions: speech emotion learning and recognition. In speech emotion learning, consi

SUBMITTER: Singkul S 

PROVIDER: S-EPMC9280549 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature