  This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the
state-of-the-art for cross-speaker prosody transfer on any text. This is one of
the most challenging, and rarely directly addressed, task in speech synthesis,
especially for highly expressive data. Daft-Exprt uses FiLM conditioning layers
to strategically inject different prosodic information in all parts of the
architecture. The model explicitly encodes traditional low-level prosody
features such as pitch, loudness and duration, but also higher level prosodic
information that helps generating convincing voices in highly expressive
styles. Speaker identity and prosodic information are disentangled through an
adversarial training strategy that enables accurate prosody transfer across
speakers. Experimental results show that Daft-Exprt significantly outperforms
strong baselines on inter-text cross-speaker prosody transfer tasks, while
yielding naturalness comparable to state-of-the-art expressive models.
Moreover, results indicate that the model discards speaker identity information
from the prosody representation, and consistently generate speech with the
desired voice. We publicly release our code and provide speech samples from our
experiments.
