Joint Unsupervised and Supervised Training for Multilingual ASR

TL;DR

This paper introduces an end-to-end joint training method for multilingual speech recognition that combines supervised and self-supervised learning, outperforming existing methods especially in low-resource languages.

Contribution

The proposed JUST method unifies supervised and self-supervised training in a single framework, improving multilingual ASR performance over traditional two-stage approaches.

Findings

Outperforms state-of-the-art methods on MLS dataset.

Significantly reduces WER in low-resource languages.

Outperforms monolingual baselines and transfer learning methods.

Abstract

Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimized in the first pretraining stage, and the standard supervised finetuning resumes in the second stage. In this paper, we propose an end-to-end (E2E) Joint Unsupervised and Supervised Training (JUST) method to combine the supervised RNN-T loss and the self-supervised contrastive and masked language modeling (MLM) losses. We validate its performance on the public dataset Multilingual LibriSpeech (MLS), which includes 8 languages and is extremely imbalanced. On MLS, we explore (1) JUST trained from scratch, and (2) JUST finetuned from a pretrained checkpoint. Experiments show that JUST can consistently outperform other existing state-of-the-art methods, and beat the monolingual baseline by a significant margin, demonstrating JUST's capability of handling low-resource languages in multilingual ASR. Our average WER of all languages outperforms average monolingual baseline by 33.3%, and the state-of-the-art 2-stage XLSR by 32%. On low-resource languages like Polish, our WER is less than half of the monolingual baseline and even beats the supervised transfer learning method which uses external supervision.