This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.
翻译:本文描述 LeVoice 自动语音识别系统,用于跟踪智能驾驶舱语音识别挑战 2022 。 Track2 是一个语音识别任务,不受模型大小限制。 我们的要点包括基于深层次语言强化、文本到语音语音生成、通过各种技术和语音识别模型培训数据增强。 我们比较并结合了混合架构和两种端到端结构。 对于端到端建模,我们使用了基于连接性时间分类/基于注意的编码-解码架构和经常性神经网络传输器/基于注意的编码-解码架构的模型。这些模型的性能用另一个语言模型来评估,以提高字差率。结果,我们的系统在挑战测试数据集中实现了10.2 ⁇ 字符误差率,并在提交的挑战系统中排第三位。