Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

中文翻译：

---

DNAE-GAN：通过集成自编码器和生成对抗网络的无噪声声学信号发生器

线性预测编码是一种非常有效的语音生成方法，其操作过程简单。然而，线性预测编码生成的声音变化有限，并表现出过多的噪音。为了解决这些问题，本文提出了一种将降噪自编码器与生成对抗网络相结合的人工智能模型。该模型通过来自生成器潜在空间的随机输入生成具有相似语义的语音。实验结果表明，仅由生成对抗网络生成的语音表现出过多的噪音。为了解决这个问题，去噪自动编码器连接到生成器进行去噪。实验结果证明了所提出的语音生成方法的可行性。将来，