Abstract: In recent years, natural and highly accurate outputs in domain transfer tasks have been achieved by deep learning techniques.
To resolve this issue, we transform the emotions in speech by the most promising GAN model, CycleGAN. In particular, we investigate the usefulness of speech�...
The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion, especially the 2D CNN LSTM�...
Further, Generative Adversarial Network is used for speech emotion data augmentation during training to deal with data scarcity problems in SER. The performance�...
Oct 6, 2021 � model with an encoder-decoder structure, and a discriminator. The encoder receives the input text and transforms it into a latent space. The�...
In this framework, a new speaker emotion-converted generative adversarial network (SEC-GAN) is developed for speaker recognition. Given the neutral speech of�...
ABSTRACT. In this paper, we propose an adversarial network implementation for speech emotion conversion as a data augmentation method, vali-.
In this paper, we propose an affective voice conversion method that can generate an emotional phonation from neutral speech by using cycle-consistent�...
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This paper proposes a nonparallel emotional speech conversion (ESC) method based on Variational AutoEncoder-Generative Adversarial Network (VAE-GAN).
Dec 4, 2019 � In this paper, we propose an affective voice conversion method that can generate an emotional phonation from neutral speech by using cycle-consistent�...
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