  In this paper, we present Period Singer, a novel end-to-end singing voice
synthesis (SVS) model that utilizes variational inference for periodic and
aperiodic components, aimed at producing natural-sounding waveforms. Recent
end-to-end SVS models have demonstrated the capability of synthesizing
high-fidelity singing voices. However, owing to deterministic pitch
conditioning, they do not fully address the one-to-many problem. To address
this problem, we present the Period Singer architecture, which integrates
variational autoencoders for the periodic and aperiodic components.
Additionally, our methodology eliminates the dependency on an external aligner
by estimating the phoneme alignment through a monotonic alignment search within
note boundaries. Our empirical evaluations show that Period Singer outperforms
existing end-to-end SVS models on Mandarin and Korean datasets. The efficacy of
the proposed method was further corroborated by ablation studies.
