# Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2009.02814 · HTML: https://mlsanity.com/paper/2009.02814/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Balancing Discriminability and Transferability for Source-Free Domain Adaptation](https://mlsanity.com/paper/2206.08009/) (2022)
- [Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching](https://mlsanity.com/paper/2104.07491/) (2021)
- [Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations](https://mlsanity.com/paper/2308.13007/) (2023)
- [MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition](https://mlsanity.com/paper/2302.11224/) (2023)
- [ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement](https://mlsanity.com/paper/2303.10727/) (2023)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection](https://mlsanity.com/paper/2508.12842/) (2025)

## Abstract (arXiv)

This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Adapt contains English speech recorded on mobile and embedded-scale microphones, and spans 72 different domains that are representative of the challenging practical scenarios encountered by ASR models. More specifically, Libri-Adapt facilitates the study of domain shifts in ASR models caused by a) different acoustic environments, b) variations in speaker accents, c) heterogeneity in the hardware and platform software of the microphones, and d) a combination of the aforementioned three shifts. We also provide a number of baseline results quantifying the impact of these domain shifts on the Mozilla DeepSpeech2 ASR model.