To tackle this real-time requirement, optimizing TTS algorithms and employing efficient hardware accelerators are essential to processing large amounts of data quickly and accurately.
This promotes diversity and inclusivity in voice synthesis, even for languages with minimal data.įor quick applications like virtual assistants, speedy text-to-speech is crucial. One solution is using transfer learning, where knowledge from well-supported languages helps in developing TTS for less-resourced languages. Languages with limited resources face issues in building strong TTS models. It helps them create adaptable voices and lessens the impact of accent differences.
To address this, advanced TTS systems incorporate extensive training datasets, including various accents and dialects, and advanced algorithms for more accurate and inclusive speech synthesis. Variations in accents and dialects make it tough for text-to-speech (TTS) voices to sound consistent and natural.
Human voice text to speech: Things you should know for realism Challenges and solutions in text-to-speech human voice