An automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity

• Main Authors: Huang, Dong-Yan, Xie, Lei, Zhang, Shaofei, Lee, Yvonne Siu Wa, Wu, Jie, Ming, Huaiping, Tian, Xiaohai, Ding, Chuang, Li, Mei, Nguyen, Quy Hy, Dong, Minghui, Chng, Eng Siong, Li, Haizhou
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2019
• Subjects: Engineering::Computer science and engineering; Voice Conversion; Objective Measures
• Online Access: https://hdl.handle.net/10356/89623; http://hdl.handle.net/10220/49691
• Institution: Nanyang Technological University

Voice conversion aims to modify the characteristics of one speaker to make it sound like spoken by another speaker without changing the language content. This task has attracted considerable attention and various approaches have been proposed since two decades ago. The evaluation of voice conversion approaches, usually through time-intensive subject listening tests, requires a huge amount of human labor. This paper proposes an automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity. Experimental results show that our automatic evaluation results match the subjective listening results quite well. We further use our strategy to select best converted samples from multiple voice conversion systems and our submission achieves promising results in the voice conversion challenge (VCC2016).