# Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

## TL;DR

This paper introduces a GAN-based method for generating self-imitating feedback in pronunciation training, transforming non-native speech spectrograms to native-like feedback with improved correction capabilities.

## Contribution

It applies GANs with cycle consistency loss to produce more natural and effective pronunciation feedback, surpassing traditional prosodic transplantation techniques.

## Key findings

- GAN successfully transforms non-native spectrograms to native-like feedback.
- The method captures segmental corrections not achievable by PSOLA.
- Perceptual tests favor the generative approach over baseline methods.

## Abstract

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.

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Source: https://tomesphere.com/paper/1904.09407