In this paper, we aim at improving the performance of synthesized speech in statistical parametric speech synthesis (SPSS) based on a generative adversarial network (GAN). In particular, we propose a novel architecture combining the traditional acoustic loss function and the GAN's discriminative loss under a multi-task learning (MTL) framework. The mean squared error (MSE) is usually used to estimate the parameters of deep neural networks, which only considers the numerical difference between the raw audio and the synthesized one. To mitigate this problem, we introduce the GAN as a second task to determine if the input is a natural speech with specific conditions. In this MTL framework, the MSE optimization improves the stability of GAN, and at the same time GAN produces samples with a distribution closer to natural speech. Listening tests show that the multi-task architecture can generate more natural speech that satisfies human perception than the conventional methods. Index Terms-Statistical parametric speech synthesis, deep neural network, generative adversarial network, conditional generative adversarial network, multi-task learning
Similar work has also been done in REF , where GANs were used to generate speech samples with a distribution close to natural speech.
Statistical parametric speech synthesis using generative adversarial networks under a multi-task learning framework
2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Computer Science