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

Abstract

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.

5 pages, Published at IEEE ICASSP 2020

References in corpus (5)

• Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
• Domain Adaptation for Statistical Classifiers
• Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification
• Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems
• Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition

Cited by in corpus (4)

• Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays
• Inverse-Hessian Regularization for Continual Learning in ASR
• DDS: A new device-degraded speech dataset for speech enhancement
• Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching