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Improving Emotion Classification through Variational Inference of Latent Variables
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conventional models for emotion recognition from speech signal are trained in supervised fashion using speech utterances with emotion labels. In this study we hypothesize that speech signal depends on multiple latent variables including the emotional state, age, gender, and speech content. We propose an Adversarial Autoencoder (AAE) to perform variational inference over the latent variables and reconstruct the input feature representations. Reconstruction of feature representations is used as
doi:10.1109/icassp.2019.8682823
dblp:conf/icassp/ParthasarathyRS19
fatcat:3badcjluyvgpnfsaiv7rjw6qlu