论文标题
零资源演讲挑战2020：发现离散子字和单词单元
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
论文作者
论文摘要
我们提出了零资源演讲挑战2020，该挑战旨在从没有任何标签的原始音频信号中学习语音表示。它结合了以前两个基准（2017和2019）的数据集和指标，并具有两个任务，这些任务涉及两个级别的语音表示。第一个任务是发现优化语音合成质量的低比率子字表示。第二个是从未分段的原始语音中发现类似单词的单元。我们介绍了二十个提交模型的结果，并讨论了主要发现对无监督语音学习的含义。
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics from two previous benchmarks (2017 and 2019) and features two tasks which tap into two levels of speech representation. The first task is to discover low bit-rate subword representations that optimize the quality of speech synthesis; the second one is to discover word-like units from unsegmented raw speech. We present the results of the twenty submitted models and discuss the implications of the main findings for unsupervised speech learning.
