Multilingual Graphemic Hybrid ASR with Massive Data Augmentation

Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang, Kritika Singh, Yatharth Saraf, Geoffrey Zweig


Abstract
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.
Anthology ID:
2020.sltu-1.7
Volume:
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | SLTU | WS
SIG:
Publisher:
European Language Resources association
Note:
Pages:
46–52
Language:
English
URL:
https://aclanthology.org/2020.sltu-1.7
DOI:
Bibkey:
Cite (ACL):
Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang, Kritika Singh, Yatharth Saraf, and Geoffrey Zweig. 2020. Multilingual Graphemic Hybrid ASR with Massive Data Augmentation. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 46–52, Marseille, France. European Language Resources association.
Cite (Informal):
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation (Liu et al., SLTU 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.sltu-1.7.pdf
Data
AudioSet