# LibriTTS-P: A Corpus with Speaking Style and Speaker Identity Prompts for Text-to-Speech and Style Captioning

arXiv: https://arxiv.org/abs/2406.07969 · HTML: https://mlsanity.com/paper/2406.07969/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [PromptTTS: Controllable Text-to-Speech with Text Descriptions](https://mlsanity.com/paper/2211.12171/) (2022)
- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://mlsanity.com/paper/2305.18802/) (2023)
- [InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt](https://mlsanity.com/paper/2301.13662/) (2023)
- [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://mlsanity.com/paper/2402.01912/) (2024)
- [TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Models](https://mlsanity.com/paper/2308.14430/) (2023)
- [Neural source-filter waveform models for statistical parametric speech synthesis](https://mlsanity.com/paper/1904.12088/) (2019)
- [PromptStyle: Controllable Style Transfer for Text-to-Speech with Natural Language Descriptions](https://mlsanity.com/paper/2305.19522/) (2023)
- [PromptTTS++: Controlling Speaker Identity in Prompt-Based Text-to-Speech Using Natural Language Descriptions](https://mlsanity.com/paper/2309.08140/) (2023)
- [Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control](https://mlsanity.com/paper/2309.13509/) (2023)
- [StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models](https://mlsanity.com/paper/2311.16509/) (2023)
- [PromptSpeaker: Speaker Generation Based on Text Descriptions](https://mlsanity.com/paper/2310.05001/) (2023)
- [Controllable Speaking Styles Using a Large Language Model](https://mlsanity.com/paper/2305.10321/) (2023)

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

- [Scaling Rich Style-Prompted Text-to-Speech Datasets](https://mlsanity.com/paper/2503.04713/) (2025)
- [SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning](https://mlsanity.com/paper/2504.09081/) (2025)
- [CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech](https://mlsanity.com/paper/2506.02863/) (2025)
- [Adversarial Training: A Survey](https://mlsanity.com/paper/2410.15042/) (2024)
- [Description-based Controllable Text-to-Speech with Cross-Lingual Voice Control](https://mlsanity.com/paper/2409.17452/) (2024)
- [Speechless: Speech Instruction Training Without Speech for Low Resource Languages](https://mlsanity.com/paper/2505.17417/) (2025)
- [RASMALAI: Resources for Adaptive Speech Modeling in Indian Languages with Accents and Intonations](https://mlsanity.com/paper/2505.18609/) (2025)
- [Voice Impression Control in Zero-Shot TTS](https://mlsanity.com/paper/2506.05688/) (2025)
- [Unispeaker: A Unified Approach for Multimodality-driven Speaker Generation](https://mlsanity.com/paper/2501.06394/) (2025)
- [MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts](https://mlsanity.com/paper/2508.11326/) (2025)
- [HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios](https://mlsanity.com/paper/2511.08496/) (2025)
- [Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data](https://mlsanity.com/paper/2603.07534/) (2026)
- [EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech Annotations](https://mlsanity.com/paper/2412.06581/) (2024)
- [VoiceDesigner: Text-to-Voice Generation and Editing via Unified Diffusion Modeling and Data Augmentation](https://mlsanity.com/paper/2608.13613/) (2026)
- [Expressive Speech Retrieval using Natural Language Descriptions of Speaking Style](https://mlsanity.com/paper/2508.11187/) (2025)

## Abstract (arXiv)

We introduce LibriTTS-P, a new corpus based on LibriTTS-R that includes utterance-level descriptions (i.e., prompts) of speaking style and speaker-level prompts of speaker characteristics. We employ a hybrid approach to construct prompt annotations: (1) manual annotations that capture human perceptions of speaker characteristics and (2) synthetic annotations on speaking style. Compared to existing English prompt datasets, our corpus provides more diverse prompt annotations for all speakers of LibriTTS-R. Experimental results for prompt-based controllable TTS demonstrate that the TTS model trained with LibriTTS-P achieves higher naturalness than the model using the conventional dataset. Furthermore, the results for style captioning tasks show that the model utilizing LibriTTS-P generates 2.5 times more accurate words than the model using a conventional dataset. Our corpus, LibriTTS-P, is available at https://github.com/line/LibriTTS-P.