Multilingual Speech Recognition With A Single End-To-End Model
This addresses the challenge of building efficient multilingual ASR systems for diverse languages, though it is incremental as it builds on existing sequence-to-sequence methods.
The authors tackled multilingual speech recognition for 9 Indian languages with minimal script overlap by training a single end-to-end sequence-to-sequence model, achieving a 21% relative improvement over per-language models and an additional 7% gain with language identifiers.
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.