  Recent character and phoneme-based parametric TTS systems using deep learning
have shown strong performance in natural speech generation. However, the choice
between character or phoneme input can create serious limitations for practical
deployment, as direct control of pronunciation is crucial in certain cases. We
demonstrate a simple method for combining multiple types of linguistic
information in a single encoder, named representation mixing, enabling flexible
choice between character, phoneme, or mixed representations during inference.
Experiments and user studies on a public audiobook corpus show the efficacy of
our approach.
