A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
Xu Li, Shansong Liu, Ying Shan · Interspeech 2022 · 2022
Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model speaker identity.However, singing contains more expressive speaker characteristics than conversational speech.It is suspected that a single embedding vector may only capture averaged and coarse-grained speaker characteristics, which is insufficient for the SVC task.To this end, this work proposes a novel hierarchical speaker representation framework for SVC, which can capture fine-grained speaker characteristics at different granularity.It consists of an up-sampling stream and three down-sampling streams.The up-sampling stream transforms the linguistic features into audio samples, while one downsampling stream of the three operates in the reverse direction.It is expected that the temporal statistics of each down-sampling block can represent speaker characteristics at different granularity, which will be engaged in the up-sampling blocks to enhance the speaker modeling.Experiment results verify that the proposed method outperforms both the LUT and SRN based SVC systems.Moreover, the proposed system supports the one-shot SVC with only a few seconds of reference audio.