To extend and improve the ManaTTS dataset, several strategies can be employed. First, incorporating a diverse range of speakers is essential. This can be achieved by recruiting multiple speakers from different regions of Iran and other Persian-speaking communities, ensuring representation of various accents and dialects. This diversity will enhance the dataset's applicability across different user demographics and improve the TTS model's ability to generalize across various speech patterns.
Second, expanding the dataset to include a broader range of domains is crucial. Currently, the dataset primarily draws from the Nasl-e-Mana magazine, which may limit the vocabulary and context. By integrating audio from various sources such as news articles, literature, educational materials, and conversational speech, the dataset can cover a wider array of topics and terminologies. This will not only enrich the vocabulary but also improve the model's performance in different contexts.
Third, continuous updates to the dataset can be facilitated by leveraging the monthly publication of the Nasl-e-Mana magazine. Automating the data collection process through web scraping and integrating new audio files regularly can help maintain the dataset's relevance and size. Additionally, implementing a feedback mechanism where users can contribute audio samples or corrections can further enhance the dataset's quality and diversity.
Finally, employing advanced data augmentation techniques can help simulate variations in speech, such as changes in pitch, speed, and background noise, thereby enriching the dataset without the need for extensive new recordings.