# Speech-to-Singing Conversion in an Encoder-Decoder Framework

arXiv: https://arxiv.org/abs/2002.06595 · HTML: https://mlsanity.com/paper/2002.06595/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [End-to-end lyrics Recognition with Voice to Singing Style Transfer](https://mlsanity.com/paper/2102.08575/) (2021)
- [Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer](https://mlsanity.com/paper/2208.12410/) (2022)
- [AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment](https://mlsanity.com/paper/2305.04476/) (2023)

## Abstract (arXiv)

In this paper our goal is to convert a set of spoken lines into sung ones. Unlike previous signal processing based methods, we take a learning based approach to the problem. This allows us to automatically model various aspects of this transformation, thus overcoming dependence on specific inputs such as high quality singing templates or phoneme-score synchronization information. Specifically, we propose an encoder--decoder framework for our task. Given time-frequency representations of speech and a target melody contour, we learn encodings that enable us to synthesize singing that preserves the linguistic content and timbre of the speaker while adhering to the target melody. We also propose a multi-task learning based objective to improve lyric intelligibility. We present a quantitative and qualitative analysis of our framework.