We describe our development of css10, a collection of single speaker speech
datasets for ten languages. It is composed of short audio clips from LibriVox
audiobooks and their aligned texts. To validate its quality we train two neural
text-to-speech models on each dataset. Subsequently,
本文研究了利用网络音频数据自动识别口语语言的任务。通过从特定语言的 Wikipedia 数据中生成半随机搜索短语,并从 YouTube 中检索视频来提取具有语音的视频片段,并使用语音活动检测和说话人分离提取包含语音的视频片段,最终构建了可用于多种口语识别任务的语言识别模型,自动检索的数据结果优于使用手工标记的专有数据集。