论文标题
改善对非目标域数据的嘈杂学生培训以自动语音识别
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition
论文作者
论文摘要
嘈杂的学生培训（NST）最近在自动语音识别（ASR）方面表现出极强的表现。在本文中，我们提出了一个名为LM Filter的数据选择策略，以提高NST在ASR任务中非目标域数据上的性能。产生有或没有语言模型的假设，并将其之间的CER差异用作滤波器阈值。结果表明，与没有数据过滤基线相比，显着改善了10.4％。我们可以在Aishell-1测试集中实现3.31％的CER，这是我们的知识所致，而没有任何其他监督数据。我们还对受监督的1000小时Aishell-2数据集进行评估，并可以实现4.73％CER的竞争结果。
Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition(ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performance of NST on non-target domain data in ASR tasks. Hypotheses with and without a Language Model are generated and the CER differences between them are utilized as a filter threshold. Results reveal that significant improvements of 10.4% compared with no data filtering baselines. We can achieve 3.31% CER in AISHELL-1 test set, which is best result from our knowledge without any other supervised data. We also perform evaluations on the supervised 1000 hour AISHELL-2 dataset and competitive results of 4.73% CER can be achieved.
