  The style of the speech varies from person to person and every person
exhibits his or her own style of speaking that is determined by the language,
geography, culture and other factors. Style is best captured by prosody of a
signal. High quality multi-speaker speech synthesis while considering prosody
and in a few shot manner is an area of active research with many real-world
applications. While multiple efforts have been made in this direction, it
remains an interesting and challenging problem. In this paper, we present a
novel few shot multi-speaker speech synthesis approach (FSM-SS) that leverages
adaptive normalization architecture with a non-autoregressive multi-head
attention model. Given an input text and a reference speech sample of an unseen
person, FSM-SS can generate speech in that person's style in a few shot manner.
Additionally, we demonstrate how the affine parameters of normalization help in
capturing the prosodic features such as energy and fundamental frequency in a
disentangled fashion and can be used to generate morphed speech output. We
demonstrate the efficacy of our proposed architecture on multi-speaker VCTK and
LibriTTS datasets, using multiple quantitative metrics that measure generated
speech distortion and MoS, along with speaker embedding analysis of the
generated speech vs the actual speech samples.
