Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.
@article{arxiv.1711.01694,
title = {Multilingual Speech Recognition With A Single End-To-End Model},
author = {Shubham Toshniwal and Tara N. Sainath and Ron J. Weiss and Bo Li and Pedro Moreno and Eugene Weinstein and Kanishka Rao},
journal= {arXiv preprint arXiv:1711.01694},
year = {2018}
}