  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.

Audio and Speech Processing;Machine Learning;Sound