Towards developing high-performing ASR for low-resource languages, approaches
to address the lack of resources are to make use of data from multiple
languages, and to augment the training data by creating acoustic variations. In
this work we present a single grapheme-based ASR model learned on 7
geographically proximal languages, using standard hybrid BLSTM-HMM acoustic
models with lattice-free MMI objective. We build the single ASR grapheme set
via taking the union over each language-specific grapheme set, and we find such
multilingual graphemic hybrid ASR model can perform language-independent
recognition on all 7 languages, and substantially outperform each monolingual
ASR model. Secondly, we evaluate the efficacy of multiple data augmentation
alternatives within language, as well as their complementarity with
multilingual modeling. Overall, we show that the proposed multilingual
graphemic hybrid ASR with various data augmentation can not only recognize any
within training set languages, but also provide large ASR performance
improvements.

本文介绍了一种单一的基于字形的 ASR 模型，采用标准的混合 BLSTM-HMM 声学模型以及晶格自由 MMI 目标进行学习，能对七种语言进行无歧义的识别，并且比每个单一语言的 ASR 模型表现更佳。同时，我们还评估了多种数据增强的方法，并且展示了这种提出的多语言字素混合 ASR 与各种数据增强不仅能识别任何训练集内的语言，还能大大提高 ASR 性能。