Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
This dataset addresses domain shift challenges in ASR for researchers, but it is incremental as it builds on existing corpora.
The paper introduces Libri-Adapt, a new speech dataset for unsupervised domain adaptation in speech recognition, built on LibriSpeech with 72 domains covering acoustic environments, accents, and hardware variations, and provides baseline results on the Mozilla DeepSpeech2 model.
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.