A text-to-speech model fails to capture natural pauses during output How can timing be modeled better

Question

With the help of code can you tell me if A text-to-speech model fails to capture natural pauses during output. How can timing be modeled better?

Answer 1

Use prosody modeling with punctuation-aware pauses, phoneme duration prediction, and deep learning-based rhythm control.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Prosody Modeling: Uses Tacotron 2, which learns speech rhythm and intonation.
• Punctuation-Aware Pauses: Converts punctuation into natural breaks.
• Neural Timing Prediction: Tacotron 2 implicitly models phoneme duration.
• Deep Learning-Based Speech Synthesis: Uses mel spectrograms for more natural timing.

Hence, by incorporating prosody modeling, punctuation-based pauses, and neural phoneme duration prediction, text-to-speech models can achieve more natural timing and rhythm, enhancing speech quality.