论文标题
ISCSLP 2022 MAGICHUB代码ASR ASR挑战的NPU-ASLP系统
The NPU-ASLP System for The ISCSLP 2022 Magichub Code-Swiching ASR Challenge
论文作者
论文摘要
本文介绍了我们提交给ISCSLP 2022 Magichub代码转换ASR挑战的NPU-ASLP系统。在这项挑战中，我们首先探讨了几种流行的端到端ASR架构和培训策略，包括双重编码器，语言意识编码器（LAE）和专家的混合物（MOE）。为了提高系统的语言建模能力，我们进一步尝试了内部语言模型以及长上下文语言模型。鉴于挑战中的培训数据有限，我们进一步研究了数据扩展的影响，包括速度扰动，俯仰换速，语音编解码器，规格和综合数据（TTS）（TTS）。最后，我们探索基于流动站的得分融合，以充分利用来自不同模型的互补假设。我们提交的系统在测试集上的混合错误率（MER）达到了16.87％，并在挑战排名中排名第二。
This paper describes our NPU-ASLP system submitted to the ISCSLP 2022 Magichub Code-Switching ASR Challenge. In this challenge, we first explore several popular end-to-end ASR architectures and training strategies, including bi-encoder, language-aware encoder (LAE) and mixture of experts (MoE). To improve our system's language modeling ability, we further attempt the internal language model as well as the long context language model. Given the limited training data in the challenge, we further investigate the effects of data augmentation, including speed perturbation, pitch shifting, speech codec, SpecAugment and synthetic data from text-to-speech (TTS). Finally, we explore ROVER-based score fusion to make full use of complementary hypotheses from different models. Our submitted system achieves 16.87% on mix error rate (MER) on the test set and comes to the 2nd place in the challenge ranking.
