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.
🌟 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).
📁 Dataset Structure
Extract LJ-TTS.zip.
Each subfolder (real data folder, plus individual TTS folders) contains audio files with identical filenames, enabling direct pairing.
📚 Citation
If you use LJ-TTS in your work, please cite:
@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}
}