# LJ-TTS: A Paired Real and Synthetic Speech Dataset for Single-Speaker TTS Analysis

LJ-TTS is a large-scale dataset containing **real human speech** and **synthetic speech** generated by **11 state-of-the-art text-to-speech (TTS) models**.  
The dataset is designed to support research in **speech synthesis**, **deepfake detection**, **speech analysis**, and **comparative evaluation of generative models** under a controlled single-speaker setting.

By providing utterance-level alignment between real and synthetic samples, LJ-TTS enables fine-grained comparisons across TTS architectures, isolating synthesis differences without the confounding effect of speaker variability.  
The dataset supports systematic analyses across multiple dimensions, including source attribution, phoneme-level acoustic studies, and robustness evaluations of synthetic speech detectors.

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## 🌟 Key Features

- **Single-speaker design**  
  Ensures controlled comparisons without multi-speaker variation.

- **Real + Synthetic speech pairs**  
  Each utterance in the REAL folder has **corresponding synthesized versions** from all TTS systems.

- **11 diverse TTS models**  
  Spanning both **autoregressive** and **non-autoregressive** architectures.

- **1:1 alignment**  
  Matching filenames and transcriptions across real and synthetic speech enable:
  - deepfake detection  
  - spoofing analysis  
  - model source tracing  
  - perceptual evaluation  
  - phoneme-level studies  
  - benchmarking and reproducible comparisons  

- **High-quality data**  
  Built upon clean recordings of Linda Jonhson (LJSpeech).

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## 📁 Dataset Structure

Extract `LJ-TTS.zip`.
Each subfolder (real data folder, plus individual TTS folders) contains audio files with **identical filenames**, enabling direct pairing.

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## 📚 Citation

If you use LJ-TTS in your work, please cite:
```bibtex
@misc{negroni2025ljtts,
  title        = {LJ-TTS: A Paired Real and Synthetic Speech Dataset for Single-Speaker TTS Analysis},
  author       = {Negroni, Viola and Salvi, Davide and Comanducci, Luca and Majid Wani, Taiba and Uecker, Madleen and Amerini, Irene and Tubaro, Stefano and Bestagini, Paolo},
  year         = {2025}
}