  Accurate control of the total duration of generated speech by adjusting the
speech rate is crucial for various text-to-speech (TTS) applications. However,
the impact of adjusting the speech rate on speech quality, such as
intelligibility and speaker characteristics, has been underexplored. In this
work, we propose a novel total-duration-aware (TDA) duration model for TTS,
where phoneme durations are predicted not only from the text input but also
from an additional input of the total target duration. We also propose a
MaskGIT-based duration model that enhances the diversity and quality of the
predicted phoneme durations. Our results demonstrate that the proposed TDA
duration models achieve better intelligibility and speaker similarity for
various speech rate configurations compared to the baseline models. We also
show that the proposed MaskGIT-based model can generate phoneme durations with
higher quality and diversity compared to its regression or flow-matching
counterparts.
