论文标题
ISCSLP 2022智能驾驶舱语音识别挑战的Levoice ASR系统
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge
论文作者
论文摘要
本文描述了Levoice自动语音识别系统，以跟踪智能驾驶舱语音识别挑战2022。TRACK2是一项语音识别任务，而没有限制模型大小的范围。我们的要点包括基于深度学习的语音增强，基于文本到语音的语音产生，通过各种技术和语音识别模型融合培训数据的增强。我们比较并融合了混合体系结构和两种端到端体系结构。对于端到端的建模，我们使用了基于连接主义者时间分类/基于注意的编码器架构的模型和经常性的神经网络传感器/基于注意的编码器decoder架构。这些模型的性能通过额外的语言模型评估，以提高单词错误率。结果，我们的系统在挑战测试集数据上达到了10.2 \％的字符错误率，在挑战中提交的系统中排名第三。
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.
