# Data Efficient Voice Cloning for Neural Singing Synthesis

arXiv: https://arxiv.org/abs/1902.07292 · HTML: https://mlsanity.com/paper/1902.07292/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [V2C: Visual Voice Cloning](https://mlsanity.com/paper/2111.12890/) (2021)
- [LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters](https://mlsanity.com/paper/2105.10371/) (2021)
- [Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis](https://mlsanity.com/paper/2210.07559/) (2022)
- [On the Cost and Benefits of Training Context with Utterance or Full Conversation Training: A Comparative Stud](https://mlsanity.com/paper/2505.07202/) (2025)

## Abstract (arXiv)

There are many use cases in singing synthesis where creating voices from small amounts of data is desirable. In text-to-speech there have been several promising results that apply voice cloning techniques to modern deep learning based models. In this work, we adapt one such technique to the case of singing synthesis. By leveraging data from many speakers to first create a multispeaker model, small amounts of target data can then efficiently adapt the model to new unseen voices. We evaluate the system using listening tests across a number of different use cases, languages and kinds of data.