  Cross-speaker emotion transfer speech synthesis aims to synthesize emotional
speech for a target speaker by transferring the emotion from reference speech
recorded by another (source) speaker. In this task, extracting
speaker-independent emotion embedding from reference speech plays an important
role. However, the emotional information conveyed by such emotion embedding
tends to be weakened in the process to squeeze out the source speaker's timbre
information. In response to this problem, a prosody compensation module (PCM)
is proposed in this paper to compensate for the emotional information loss.
Specifically, the PCM tries to obtain speaker-independent emotional information
from the intermediate feature of a pre-trained ASR model. To this end, a
prosody compensation encoder with global context (GC) blocks is introduced to
obtain global emotional information from the ASR model's intermediate feature.
Experiments demonstrate that the proposed PCM can effectively compensate the
emotion embedding for the emotional information loss, and meanwhile maintain
the timbre of the target speaker. Comparisons with state-of-the-art models show
that our proposed method presents obvious superiority on the cross-speaker
emotion transfer task.
