# VRAIN-UPV MLLP's system for the Blizzard Challenge 2021

arXiv: https://arxiv.org/abs/2110.15792 · HTML: https://mlsanity.com/paper/2110.15792/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2021.

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

- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition](https://mlsanity.com/paper/2310.08132/) (2023)
- [On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures](https://mlsanity.com/paper/2407.17997/) (2024)

## Abstract (arXiv)

This paper presents the VRAIN-UPV MLLP's speech synthesis system for the SH1 task of the Blizzard Challenge 2021. The SH1 task consisted in building a Spanish text-to-speech system trained on (but not limited to) the corpus released by the Blizzard Challenge 2021 organization. It included 5 hours of studio-quality recordings from a native Spanish female speaker. In our case, this dataset was solely used to build a two-stage neural text-to-speech pipeline composed of a non-autoregressive acoustic model with explicit duration modeling and a HiFi-GAN neural vocoder. Our team is identified as J in the evaluation results. Our system obtained very good results in the subjective evaluation tests. Only one system among other 11 participants achieved better naturalness than ours. Concretely, it achieved a naturalness MOS of 3.61 compared to 4.21 for real samples.