  Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). Accented TTS synthesis
is challenging as L2 is different from L1 in both in terms of phonetic
rendering and prosody pattern. Furthermore, there is no easy solution to the
control of the accent intensity in an utterance. In this work, we propose a
neural TTS architecture, that allows us to control the accent and its intensity
during inference. This is achieved through three novel mechanisms, 1) an accent
variance adaptor to model the complex accent variance with three prosody
controlling factors, namely pitch, energy and duration; 2) an accent intensity
modeling strategy to quantify the accent intensity; 3) a consistency constraint
module to encourage the TTS system to render the expected accent intensity at a
fine level. Experiments show that the proposed system attains superior
performance to the baseline models in terms of accent rendering and intensity
control. To our best knowledge, this is the first study of accented TTS
synthesis with explicit intensity control.
