This paper focuses on using voice conversion (VC) to improve the speech
intelligibility of surgical patients who have had parts of their articulators
removed. Due to the difficulty of data collection, VC without parallel data is
highly desired. Although techniques for unparallel VC, for example, CycleGAN,
have been developed, they usually focus on transforming the speaker identity,
and directly transforming the speech of one speaker to that of another speaker
and as such do not address the task here. In this paper, we propose a new
approach for unparallel VC. The proposed approach transforms impaired speech to
normal speech while preserving the linguistic content and speaker
characteristics. To our knowledge, this is the first end-to-end GAN-based
unsupervised VC model applied to impaired speech. The experimental results show
that the proposed approach outperforms CycleGAN.

本研究提出了一种新的音频转换方法，是一种 GAN-based 无监督的语音转换模型，用于改善协助医疗手术的病人的语音理解性，该方法可以将受损的语音转换为正常的语音而保留语言内容和说话人特征。