  Recent advances in Text-to-Speech (TTS) have improved quality and naturalness
to near-human capabilities when considering isolated sentences. But something
which is still lacking in order to achieve human-like communication is the
dynamic variations and adaptability of human speech. This work attempts to
solve the problem of achieving a more dynamic and natural intonation in TTS
systems, particularly for stylistic speech such as the newscaster speaking
style. We propose a novel embedding selection approach which exploits
linguistic information, leveraging the speech variability present in the
training dataset. We analyze the contribution of both semantic and syntactic
features. Our results show that the approach improves the prosody and
naturalness for complex utterances as well as in Long Form Reading (LFR).
