论文标题
基于传感器的自动语音识别的域适应性损伤控制
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
论文作者
论文摘要
自动语音识别模型通常可以改善其在新领域中的准确性。模型适应新领域的潜在缺点是灾难性的遗忘，在原始域上的错误率显着降低。本文解决了我们要同时将自动语音识别模型适应新领域并限制原始域上准确性的降低而无需访问原始培训数据集的准确性降解时，解决了这种情况。我们建议使用多种技术，例如有限的培训策略和针对传感器编码器，预测和木匠网络的正规适配器模块。我们将这些方法应用于Google语音命令，并将这些方法应用于英国和爱尔兰英语方言语音数据集，并在新的目标域上获得强大的结果，同时限制原始域上的降级。
Automatic speech recognition models are often adapted to improve their accuracy in a new domain. A potential drawback of model adaptation to new domains is catastrophic forgetting, where the Word Error Rate on the original domain is significantly degraded. This paper addresses the situation when we want to simultaneously adapt automatic speech recognition models to a new domain and limit the degradation of accuracy on the original domain without access to the original training dataset. We propose several techniques such as a limited training strategy and regularized adapter modules for the Transducer encoder, prediction, and joiner network. We apply these methods to the Google Speech Commands and to the UK and Ireland English Dialect speech data set and obtain strong results on the new target domain while limiting the degradation on the original domain.
