XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (2111.09296v3)

Abstract: This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.

XLS-R: Advancements in Self-supervised Cross-lingual Speech Representation Learning

The paper "XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale" presents a comprehensive investigation into the scaling of self-supervised learning applied to cross-lingual speech processing. Leveraging the architecture of wav2vec 2.0, XLS-R is trained on a mammoth dataset of 436K hours of speech across 128 languages, surpassing the scale of previous models and exploring the effects of model capacity on multilingual tasks.

Methodology and Data

XLS-R builds on the wav2vec 2.0 framework by integrating features from multiple sources of unlabeled speech data. The model is trained on a vast dataset comprising VoxPopuli, Multilingual Librispeech (MLS), CommonVoice, VoxLingua107, and Babel—each contributing uniquely to the language and acoustic diversity in the training set. The authors deploy models with varying parameters (up to 2 billion) and utilize advanced techniques to optimize memory usage, such as activation checkpointing and a fully sharded data-parallel training backend.

Evaluation and Results

XLS-R's performance is rigorously evaluated across a range of benchmarks, including Automatic Speech Translation (AST), Automatic Speech Recognition (ASR), and Speech Classification tasks. The strong numerical results exhibit remarkable improvements in several key areas:

• Speech Translation: XLS-R notches significant gains on the CoVoST-2 benchmark. Notably, it achieves an average improvement of 7.4 BLEU over 21 X $\rightarrow$ English translation directions, with the largest improvements seen in mid- and low-resource language settings.
• Speech Recognition: The model outperforms all prior work on multiple datasets including BABEL, CommonVoice, MLS, and VoxPopuli. XLS-R shows substantial reductions in error rates, exemplifying its robustness across diverse language data scenarios.
• Speech Classification: On VoxLingua107 for language identification and VoxCeleb1 for speaker identification, XLS-R demonstrates state-of-the-art accuracy, thereby confirming its versatility beyond conventional ASR and AST tasks.

Implications and Future Directions

The paper illustrates that scaling up speech representation models significantly aids in enhancing cross-lingual transfer learning capabilities. By comparing XLS-R with English-only pretrained models, the authors demonstrate that with sufficient model size, cross-lingual models can rival or even surpass their monolingual counterparts, especially in tasks with less training data.

The implications of XLS-R extend into the field of low-resource language processing, providing a template for deploying efficient models across multiple languages using a unified architecture. This approach could catalyze further innovations in speech processing by reducing reliance on abundant labeled data.

Future directions may involve further exploring the intersections of large-scale self-supervised learning and multilingual adaptation. Additionally, fine-tuning strategies, domain adaptation, and multilingual pretraining methods stand as pivotal areas to propel advancements in AI-driven speech applications.

In conclusion, XLS-R demonstrates how scaling data and model parameters in self-supervised speech models can lead to both theoretical and practical advancements in the field. Its contributions not only push the boundaries of what is possible with multilingual speech technology but also pave the way for more inclusive, language-diverse AI systems.