VRAIN-UPV MLLP's system for the Blizzard Challenge 2021
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1.
4 papers.
- 2022Low-Resource Multilingual and Zero-Shot Multispeaker TTScited by 15
- 2022Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speechcited by 13
- 2023On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognitioncited by 4
- 2024On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architecturescited by 4
- 2024On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures25 Jul 2024
- 2023On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition12 Oct 2023
- 2022Low-Resource Multilingual and Zero-Shot Multispeaker TTS21 Oct 2022
- 2022Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech24 Jun 2022