论文标题
libri-adapt：无监督域改编的新的语音数据集
Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
论文作者
论文摘要
本文介绍了一个新的数据集《库伊》（Libri-Adapt），以支持语音识别模型的无监督域适应性研究。 Libri-Adapt建立在Librispeech语料库之上，其中包含在移动和嵌入式尺度麦克风上记录的英语语音，并跨越了72个不同的域，这些域代表了ASR模型遇到的具有挑战性的实践场景。更具体地说，Libri-Adapt有助于研究由A）不同的声学环境引起的ASR模型中的域移位研究，b）扬声器口音的变化，c）麦克风的硬件和平台软件中的异质性，以及d）上述三个转移的组合。我们还提供了许多基线结果，以量化这些域移位对Mozilla DeepSpeech2 ASR模型的影响。
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.
