Adversarial training improves text-to-speech (TTS) model robustness by exposing the model to intentionally challenging or distorted inputs during training. This process forces the model to learn patterns that generalize better to real-world variations, such as noisy text, uncommon pronunciations, or syntactically complex sentences. By training on these adversarial examples, the model becomes less likely to fail when encountering edge cases or unexpected inputs, leading to more reliable and consistent speech output.
The core mechanism involves generating adversarial examples that target the model’s weaknesses. For instance, a TTS model might struggle with homographs (e.g., “read” in “I will read” vs. “I have read”). Adversarial training could include such examples with context clues, forcing the model to infer pronunciation from surrounding words. Another approach is perturbing input text with typos, missing punctuation, or uncommon abbreviations (e.g., “gonna” instead of “going to”) to simulate real-world inputs. During training, the model’s loss function is optimized to minimize errors on both clean and perturbed data, effectively teaching it to handle ambiguity and noise. Techniques like gradient-based adversarial attacks or rule-based perturbations are often used to create these examples dynamically, ensuring the model adapts to diverse challenges.
Specific benefits include improved handling of rare words, accents, and noisy inputs. For example, a TTS model trained adversarially might better pronounce technical terms (e.g., “Schrödinger”) by seeing them in varied contexts during training. Similarly, exposure to sentences with unusual syntax (e.g., “The cake was eaten by the dog, which was blue”) helps the model parse structure more accurately. This approach also reduces overfitting to “clean” datasets, enabling the model to generalize to user-generated text with errors. Developers can implement adversarial training by augmenting datasets with perturbed examples or integrating adversarial loss terms into the training loop. For instance, adding a secondary loss that penalizes inconsistent prosody when inputs are slightly altered ensures the model learns stable speech patterns. These strategies collectively enhance the model’s resilience in production environments.