Developing high-quality speech synthesis models that can handle noisy data and generate
accurate representations of speech is a challenging task. To tackle this issue, Zhang et al. [650]
propose a novel approach involving multi-length adversarial training. This method allows for
modeling different noise conditions and improves the accuracy of pitch prediction by incorporating
discriminators on the mel-spectrogram. By replacing the traditional pitch predictor model with this
approach, the authors demonstrate significant improvements in the fidelity of synthesized speech.
AReviewofDeepLearningTechniquesforSpeechProcessing