# N"ushuVoice: Reviving the Voice of Endangered N"ushu with Pitch-Aware Text-to-Speech

arXiv: https://arxiv.org/abs/2606.09295 · HTML: https://mlsanity.com/paper/2606.09295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition](https://mlsanity.com/paper/2008.03687/) (2020)
- [AI Nushu: An Exploration of Language Emergence in Sisterhood -Through the Lens of Computational Linguistics](https://mlsanity.com/paper/2310.11870/) (2023)
- [NushuRescue: Revitalization of the Endangered Nushu Language with AI](https://mlsanity.com/paper/2412.00218/) (2024)

## Abstract (arXiv)

N"ushu is an endangered phonetic script historically used by women in Jiangyong County, southern Hunan, China. While existing computational studies of N"ushu mainly focus on textual digitization and visual recognition, the acoustic reconstruction of its authentic pronunciation remains largely unexplored. Building a N"ushu text-to-speech (TTS) system is particularly challenging because available recordings are extremely limited and mostly consist of isolated syllable-level pronunciations rather than natural sentence-level utterances. In this work, we introduce N"ushuVoice, the first TTS benchmark for N"ushu. We construct a sentence-level N"ushu text-to-audio dataset that aligns standardized Unicode N"ushu text, phonetic transcriptions, standard Chinese translations, and archival recordings. To synthesize speech under this extreme low-resource setting, we propose N"ushu-PitchVITS, an F0-conditioned VITS framework that leverages N"ushu's five-level pitch notation as an explicit prosodic inductive bias. Experimental results show that N"ushu-PitchVITS outperforms strong TTS baselines in spectral fidelity, pitch reconstruction, and human-rated intelligibility. We publicly release the dataset and code at: https://anonymous.4open.science/r/Nvshu-TTS-2EB6.