  Recently, emotional speech synthesis has achieved remarkable performance. The
emotion strength of synthesized speech can be controlled flexibly using a
strength descriptor, which is obtained by an emotion attribute ranking
function. However, a trained ranking function on specific data has poor
generalization, which limits its applicability for more realistic cases. In
this paper, we propose a deep learning based emotion strength assessment
network for strength prediction that is referred to as StrengthNet. Our model
conforms to a multi-task learning framework with a structure that includes an
acoustic encoder, a strength predictor and an auxiliary emotion predictor. A
data augmentation strategy was utilized to improve the model generalization.
Experiments show that the predicted emotion strength of the proposed
StrengthNet are highly correlated with ground truth scores for seen and unseen
speech. Our codes are available at: https://github.com/ttslr/StrengthNet.
