# The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS

arXiv: https://arxiv.org/abs/2010.02434 · HTML: https://mlsanity.com/paper/2010.02434/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations](https://mlsanity.com/paper/2110.06280/) (2021)
- [A Comparative Study of Self-supervised Speech Representation Based Voice Conversion](https://mlsanity.com/paper/2207.04356/) (2022)
- [End-to-end streaming model for low-latency speech anonymization](https://mlsanity.com/paper/2406.09277/) (2024)
- [TVTSyn: Content-Synchronous Time-Varying Timbre for Streaming Voice Conversion and Anonymization](https://mlsanity.com/paper/2602.09389/) (2026)
- [On Prosody Modeling for ASR+TTS based Voice Conversion](https://mlsanity.com/paper/2107.09477/) (2021)
- [SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition](https://mlsanity.com/paper/2401.18045/) (2024)
- [ALO-VC: Any-to-any Low-latency One-shot Voice Conversion](https://mlsanity.com/paper/2306.01100/) (2023)
- [Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction](https://mlsanity.com/paper/2105.09858/) (2021)
- [Deep Learning and Synthetic Media](https://mlsanity.com/paper/2205.05764/) (2022)
- [Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines](https://mlsanity.com/paper/2412.14684/) (2024)
- [Automatic Speech Disentanglement for Voice Conversion using Rank Module and Speech Augmentation](https://mlsanity.com/paper/2306.12259/) (2023)

## Abstract (arXiv)

This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020. We consider a naive approach for voice conversion (VC), which is to first transcribe the input speech with an automatic speech recognition (ASR) model, followed using the transcriptions to generate the voice of the target with a text-to-speech (TTS) model. We revisit this method under a sequence-to-sequence (seq2seq) framework by utilizing ESPnet, an open-source end-to-end speech processing toolkit, and the many well-configured pretrained models provided by the community. Official evaluation results show that our system comes out top among the participating systems in terms of conversion similarity, demonstrating the promising ability of seq2seq models to convert speaker identity. The implementation is made open-source at: https://github.com/espnet/espnet/tree/master/egs/vcc20.