The end-to-end approach utilized in the FruitShell French synthesis system, while effective, does present several potential drawbacks. One significant limitation is the reliance on a single model architecture, which may not capture all nuances of speech synthesis, particularly in terms of prosody and emotional expression. This could lead to less natural-sounding speech, especially in complex linguistic contexts. Future research could address this by integrating multi-task learning frameworks that allow the model to learn from various aspects of speech synthesis simultaneously, such as emotion recognition and prosody prediction.
Another limitation is the potential for overfitting, particularly when training on limited datasets. The model may learn to replicate the training data too closely, resulting in poor generalization to unseen data. To mitigate this, researchers could implement regularization techniques, such as dropout or weight decay, and explore data augmentation strategies that introduce variability without compromising the integrity of the training data.
Additionally, the model's performance may be sensitive to the quality of the input data, as seen in the Spoke task where lower-quality additional data impacted results. Future work could focus on developing robust preprocessing techniques that enhance data quality and consistency, as well as exploring ensemble methods that combine predictions from multiple models to improve overall synthesis quality.