# HUI-Audio-Corpus-German: A high quality TTS dataset

arXiv: https://arxiv.org/abs/2106.06309 · HTML: https://mlsanity.com/paper/2106.06309/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://mlsanity.com/paper/2005.05106/) (2020)
- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://mlsanity.com/paper/1903.11269/) (2019)

## Built on by (most cited first, 15 of 15)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [A Representative Study on Human Detection of Artificially Generated Media Across Countries](https://mlsanity.com/paper/2312.05976/) (2023)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [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)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [PoeticTTS -- Controllable Poetry Reading for Literary Studies](https://mlsanity.com/paper/2207.05549/) (2022)
- [Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual Support](https://mlsanity.com/paper/2407.07275/) (2024)
- [Finite Scalar Quantization Enables Redundant and Transmission-Robust Neural Audio Compression at Low Bit-rates](https://mlsanity.com/paper/2509.09550/) (2025)
- [ASR in German: A Detailed Error Analysis](https://mlsanity.com/paper/2204.05617/) (2022)
- [ASR Bundestag: A Large-Scale political debate dataset in German](https://mlsanity.com/paper/2302.06008/) (2023)
- [Controllable Generation of Artificial Speaker Embeddings through Discovery of Principal Directions](https://mlsanity.com/paper/2310.17502/) (2023)
- [ArmanTTS single-speaker Persian dataset](https://mlsanity.com/paper/2304.03585/) (2023)
- [Lightweight and perceptually-guided voice conversion for electro-laryngeal speech](https://mlsanity.com/paper/2601.03892/) (2026)
- [Scaling Laws for Multimodal Data Mixtures](https://mlsanity.com/paper/2610.14423/) (2026)

## Abstract (arXiv)

The increasing availability of audio data on the internet lead to a multitude of datasets for development and training of text to speech applications, based on neural networks. Highly differing quality of voice, low sampling rates, lack of text normalization and disadvantageous alignment of audio samples to corresponding transcript sentences still limit the performance of deep neural networks trained on this task. Additionally, data resources in languages like German are still very limited. We introduce the "HUI-Audio-Corpus-German", a large, open-source dataset for TTS engines, created with a processing pipeline, which produces high quality audio to transcription alignments and decreases manual effort needed for creation.