  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.
