Improving noisy student training for low-resource languages in End-to-End ASR using CycleGAN and inter-domain losses
This work addresses the problem of improving speech recognition for low-resource languages, which is incremental as it builds on existing noisy student training and CycleGAN methods.
The paper tackles the challenge of training semi-supervised end-to-end speech recognition for low-resource languages with limited paired speech-text and unlabeled speech, achieving a 20% word error rate reduction compared to the baseline teacher model and a 10% reduction compared to the baseline best student model.
Training a semi-supervised end-to-end speech recognition system using noisy student training has significantly improved performance. However, this approach requires a substantial amount of paired speech-text and unlabeled speech, which is costly for low-resource languages. Therefore, this paper considers a more extreme case of semi-supervised end-to-end automatic speech recognition where there are limited paired speech-text, unlabeled speech (less than five hours), and abundant external text. Firstly, we observe improved performance by training the model using our previous work on semi-supervised learning "CycleGAN and inter-domain losses" solely with external text. Secondly, we enhance "CycleGAN and inter-domain losses" by incorporating automatic hyperparameter tuning, calling it "enhanced CycleGAN inter-domain losses." Thirdly, we integrate it into the noisy student training approach pipeline for low-resource scenarios. Our experimental results, conducted on six non-English languages from Voxforge and Common Voice, show a 20% word error rate reduction compared to the baseline teacher model and a 10% word error rate reduction compared to the baseline best student model, highlighting the significant improvements achieved through our proposed method.