论文标题
但是OpenSat 2019语音识别系统
BUT Opensat 2019 Speech Recognition System
论文作者
论文摘要
该论文描述了在两个领域类别（例如低资源的语言和公共安全通信）下提交的但自动的语音识别（ASR）系统。由于缺乏培训数据，第一个是具有挑战性的，因此采用了各种架构和多语言方法。组合导致了出色的性能。第二个领域由于在极端条件下的记录（例如特定的通道，在压力下的扬声器和高噪声）而具有挑战性。数据增强过程是不可避免的，无法获得合理的良好性能。
The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safety communications. The first was challenging due to lack of training data, therefore various architectures and multilingual approaches were employed. The combination led to superior performance. The second domain was challenging due to recording in extreme conditions such as specific channel, speaker under stress and high levels of noise. Data augmentation process was inevitable to get reasonably good performance.
