To enhance the data augmentation process for the Spoke task, several strategies could be implemented. First, leveraging advanced techniques such as voice conversion could be beneficial. This method would allow the model to transform the voice characteristics of existing speakers in the dataset to more closely resemble the target speaker, thereby increasing the effective size of the specific-speaker dataset. Additionally, incorporating synthetic data generation techniques, such as using Generative Adversarial Networks (GANs) to create new audio samples that mimic the target speaker's voice, could further enrich the dataset.
Another approach could involve the use of semi-supervised learning, where a smaller amount of labeled data is combined with a larger pool of unlabeled data. This could help the model learn more robust features of the target speaker's voice. Furthermore, fine-tuning the model on a diverse set of speakers before specializing on the target speaker could improve the model's adaptability and performance. Lastly, exploring multilingual datasets that include the target speaker's language could provide additional context and variability, enhancing the model's ability to synthesize speech that closely resembles the specific individual.