Speaker-independent acoustic features extraction using StarGAN-VC and its applications for double articulation analysis

Abstract

Nonparametric Bayesian double articulation analyzer (NPB-DAA) is a method to discover words and phoneme units from continuous speech signals in an unsupervised manner. However, acoustic features have speaker-dependency, and it prevent NPB-DAA from discovering words and phonem units from multi-speaker utterances. This paper proposes to use star generative adversarial network for voice conversion (StarGAN-VC) to extract speaker-independent acoustic features and optimize NPB-DAA and StarGAN-VC simultaneously by using mutual learning based on Neuro-SERKET framework. The effect of mutual learning is shown through an experiment.