  With the advent of high-quality speech synthesis, there is a lot of interest
in controlling various prosodic attributes of speech. Speaking rate is an
essential attribute towards modelling the expressivity of speech. In this work,
we propose a novel approach to control the speaking rate for non-autoregressive
TTS. We achieve this by conditioning the speaking rate inside the duration
predictor, allowing implicit speaking rate control. We show the benefits of
this approach by synthesising audio at various speaking rate factors and
measuring the quality of speaking rate-controlled synthesised speech. Further,
we study the effect of the speaking rate distribution of the training data
towards effective rate control. Finally, we fine-tune a baseline pretrained TTS
model to obtain speaking rate control TTS. We provide various analyses to
showcase the benefits of using this proposed approach, along with objective as
well as subjective metrics. We find that the proposed methods have higher
subjective scores and lower speaker rate errors across many speaking rate
factors over the baseline.
