# Multilingual Graphemic Hybrid ASR with Massive Data Augmentation

arXiv: https://arxiv.org/abs/1909.06522 · HTML: https://mlsanity.com/paper/1909.06522/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Modelling Latent Translations for Cross-Lingual Transfer](https://mlsanity.com/paper/2107.11353/) (2021)
- [RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2109.10410/) (2021)
- [Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR](https://mlsanity.com/paper/2111.06799/) (2021)
- [Cross-lingual Similarity of Multilingual Representations Revisited](https://mlsanity.com/paper/2212.01924/) (2022)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [A Joint Matrix Factorization Analysis of Multilingual Representations](https://mlsanity.com/paper/2310.15513/) (2023)
- [Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximation](https://mlsanity.com/paper/2507.06249/) (2025)
- [Investigating Idiomaticity in Word Representations](https://mlsanity.com/paper/2411.02610/) (2024)
- [Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2509.25138/) (2025)
- [Adaptive Activation Network For Low Resource Multilingual Speech Recognition](https://mlsanity.com/paper/2205.14326/) (2022)
- [Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss](https://mlsanity.com/paper/2308.06327/) (2023)

## Abstract (arXiv)

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.