  The problem of audio-to-audio (A2A) style transfer involves replacing the
style features of the source audio with those from the target audio while
preserving the content related attributes of the source audio. In this paper,
we propose an efficient approach, termed as Zero-shot Emotion Style Transfer
(ZEST), that allows the transfer of emotional content present in the given
source audio with the one embedded in the target audio while retaining the
speaker and speech content from the source. The proposed system builds upon
decomposing speech into semantic tokens, speaker representations and emotion
embeddings. Using these factors, we propose a framework to reconstruct the
pitch contour of the given speech signal and train a decoder that reconstructs
the speech signal. The model is trained using a self-supervision based
reconstruction loss. During conversion, the emotion embedding is alone derived
from the target audio, while rest of the factors are derived from the source
audio. In our experiments, we show that, even without using parallel training
data or labels from the source or target audio, we illustrate zero shot emotion
transfer capabilities of the proposed ZEST model using objective and subjective
quality evaluations.
