  In human speech, the attitude of a speaker cannot be fully expressed only by
the textual content. It has to come along with the intonation. Declarative
questions are commonly used in daily Cantonese conversations, and they are
usually uttered with rising intonation. Vanilla neural text-to-speech (TTS)
systems are not capable of synthesizing rising intonation for these sentences
due to the loss of semantic information. Though it has become more common to
complement the systems with extra language models, their performance in
modeling rising intonation is not well studied. In this paper, we propose to
complement the Cantonese TTS model with a BERT-based statement/question
classifier. We design different training strategies and compare their
performance. We conduct our experiments on a Cantonese corpus named CanTTS.
Empirical results show that the separate training approach obtains the best
generalization performance and feasibility.
