  Self-imitating feedback is an effective and learner-friendly method for
non-native learners in Computer-Assisted Pronunciation Training. Acoustic
characteristics in native utterances are extracted and transplanted onto
learner's own speech input, and given back to the learner as a corrective
feedback. Previous works focused on speech conversion using prosodic
transplantation techniques based on PSOLA algorithm. Motivated by the visual
differences found in spectrograms of native and non-native speeches, we
investigated applying GAN to generate self-imitating feedback by utilizing
generator's ability through adversarial training. Because this mapping is
highly under-constrained, we also adopt cycle consistency loss to encourage the
output to preserve the global structure, which is shared by native and
non-native utterances. Trained on 97,200 spectrogram images of short utterances
produced by native and non-native speakers of Korean, the generator is able to
successfully transform the non-native spectrogram input to a spectrogram with
properties of self-imitating feedback. Furthermore, the transformed spectrogram
shows segmental corrections that cannot be obtained by prosodic
transplantation. Perceptual test comparing the self-imitating and correcting
abilities of our method with the baseline PSOLA method shows that the
generative approach with cycle consistency loss is promising.
