论文标题
低资源语言中语音识别的多语言变压器语言模型
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages
论文作者
论文摘要
由于（1）低资源语言的数据稀缺性，（2）培训和刷新100多种单语言模型的昂贵计算成本，以及（3）托管效率低下，考虑到流量稀少，因此训练和部署用于混合语音识别的变压器LMS以低资源语言重新排行第二通过，这是一项挑战。在这项研究中，我们提出了一种新的方法，将多个低资源区域组合在一起，并优化ASR中多语言变压器LMS的性能。我们的网站组多语言变压器LMS的表现优于传统的多语言LM，以及降低维护成本和运营费用。此外，对于部署单语模型的低资源但人口流量的地区是可行的，我们表明，与基线单语LMS相比，对我们的语言环境组的多语言LMS产生更好的单语LM候选者。
It is challenging to train and deploy Transformer LMs for hybrid speech recognition 2nd pass re-ranking in low-resource languages due to (1) data scarcity in low-resource languages, (2) expensive computing costs for training and refreshing 100+ monolingual models, and (3) hosting inefficiency considering sparse traffic. In this study, we present a new way to group multiple low-resource locales together and optimize the performance of Multilingual Transformer LMs in ASR. Our Locale-group Multilingual Transformer LMs outperform traditional multilingual LMs along with reducing maintenance costs and operating expenses. Further, for low-resource but high-traffic locales where deploying monolingual models is feasible, we show that fine-tuning our locale-group multilingual LMs produces better monolingual LM candidates than baseline monolingual LMs.
