# Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use

arXiv: https://arxiv.org/abs/2505.21578 · HTML: https://mlsanity.com/paper/2505.21578/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 17 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 14 of 14)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://mlsanity.com/paper/2111.09344/) (2021)
- [Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context](https://mlsanity.com/paper/2309.08105/) (2023)
- [SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network](https://mlsanity.com/paper/2104.02133/) (2021)
- [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://mlsanity.com/paper/2406.00899/) (2024)
- [Open-Source Conversational AI with SpeechBrain 1.0](https://mlsanity.com/paper/2407.00463/) (2024)
- [Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data](https://mlsanity.com/paper/2309.13876/) (2023)
- [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://mlsanity.com/paper/2410.01036/) (2024)
- [Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping](https://mlsanity.com/paper/2404.07341/) (2024)

## Built on by (most cited first, 9 of 9)

- [Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS](https://mlsanity.com/paper/2605.30748/) (2026)
- [Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset](https://mlsanity.com/paper/2512.17915/) (2025)
- [The Speech-LLM Takes It All: A Truly Fully End-to-End Spoken Dialogue State Tracking Approach](https://mlsanity.com/paper/2510.09424/) (2025)
- [A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2609.28060/) (2026)
- [Measuring the Redundancy of Decoder Layers in SpeechLLMs](https://mlsanity.com/paper/2603.05121/) (2026)
- [Streaming Speech-to-Text Translation with a SpeechLLM](https://mlsanity.com/paper/2605.14766/) (2026)
- [Graph-Based Phonetic Error Correction of Noisy ASR](https://mlsanity.com/paper/2606.24889/) (2026)
- [AVERT: Audio-Verified Adjudication for Spoken Dialogue State Tracking](https://mlsanity.com/paper/2609.01828/) (2026)
- [A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis](https://mlsanity.com/paper/2609.30075/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) research is driven by the availability of common datasets between industrial researchers and academics, encouraging comparisons and evaluations. LibriSpeech, despite its long success as an ASR benchmark, is now limited by its size and focus on clean, read speech, leading to near-zero word error rates. More recent datasets, including MOSEL, YODAS, Gigaspeech, OWSM, Libriheavy or People's Speech suffer from major limitations including licenses that researchers in the industry cannot use, unreliable transcriptions, incorrect audio data, or the lack of evaluation sets. This work presents the Loquacious Set, a 25,000-hour curated collection of commercially usable English speech. Featuring hundreds of thousands of speakers with diverse accents and a wide range of speech types (read, spontaneous, talks, clean, noisy), the Loquacious Set is designed to work for academics and researchers in the industry to build ASR systems in real-world scenarios.