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arXiv · 2209.04041

Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages

Li Miao·Jian Wu·Piyush Behre·Shuangyu Chang·Sarangarajan Parthasarathy
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Abstract

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.

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Li Miao, Jian Wu, Piyush Behre, Shuangyu Chang, Sarangarajan Parthasarathy. 2022-09-08. Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages. https://arxiv.org/abs/2209.04041

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