LibriVox: Free Public Domain Audiobooks

By Jodi Kearns. 2014

128 citations recorded by the source corpus.

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Citations in the corpus

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech (2019, methodology): “The LJspeech and M-AILABS corpora are comprised of non-professional audiobooks from the LibriVox project [16] and distributed under a non-restrictive license, whereas the BC2013 is read by a professional speaker but distributed under a non-commercial license like BC2011.”
Review of end-to-end speech synthesis technology based on deep learning (2021, background): “LJ Speech(2) is a public domain corpus consisting of 13,100 short audio clips of a single speaker, made up of non-professional audiobooks from the LibriVox project [95].”
LMCodec: A Low Bitrate Speech Codec with Causal Transformer Models (2023, methodology): “The SoundStream model is trained on 16 kHz audio from the LibriVox dataset [16] for 1M steps.”
Disentangling speech from surroundings in a neural audio codec (2022, methodology): “We use speech waveforms from the LibriVox dataset [27], sampled at 16 kHz, and synthesize noisy speech waveforms by mixing these with noise from the Freesound dataset [28], which was screened to remove speech content and only keep clips with a CC0 license.”
Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net (2020, background): “The former is a large scale dataset where the speech samples were collected from Librivox [23], and noise samples from Audioset [24] and Freesound [25].”
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis (2021, methodology): “LibriSpeech [3] and LibriTTS [2] derive their text source from the LibriVox project which is based on audiobooks [9].”
Personalized speech enhancement: new models and Comprehensive evaluation (2021, methodology): “The dataset consists of around 544 hours of speech samples from the LibriVox corpus [24] that were obtained by selecting high mean opinion score (MOS) samples.”
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention with Dilated 1D Convolutions (2019, methodology): “The LibriSpeech corpus is a collection of approximately 1,000hr audiobooks that are a part of the LibriVox project [25].”
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement (2021, methodology): “As a clean speech corpus, the dataset collects 544 hours of speech recordings with high mean opinion score (MOS) values from the LibriVox corpus [25].”