Unsupervised Cross-lingual Representation Learning for Speech Recognition (2006.13979v2)

Abstract: This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.

Unsupervised Cross-lingual Representation Learning for Speech Recognition

This paper articulates an innovative approach to unsupervised cross-lingual representation learning for speech recognition, termed XLSR. Building on the wav2vec 2.0 framework, the authors propose a model trained on raw waveform data across multiple languages. This model leverages contrastive learning techniques over masked latent speech representations, jointly optimizing a quantization method to augment cross-lingual sharing of representations.

Key Contributions

Multilingual Pretraining

The central tenet of this paper is pretraining a single multilingual model versus multiple monolingual models. The experimental analysis demonstrates that multilingual pretraining significantly outstrips monolingual counterparts, with XLSR-10 (a model pretrained on ten languages) achieving a relative phoneme error rate reduction of 72% on the CommonVoice dataset and improving word error rates by 16% on the BABEL benchmark.

Shared Representational Space

The paper elucidates how cross-lingual pretraining on unlabeled data allows for the generation of shared discrete speech representations. The effectiveness of these representations transcends to languages that were unseen in the pretraining phase, illustrating the robustness of the transfer learning capabilities inherent in the model. This is corroborated by performance improvements over previous monolingual and multilingual baselines.

High vs. Low-Resource Languages

A nuanced exploration of the trade-offs between high and low-resource languages is offered. The XLSR model exhibits substantial enhancements for low-resource languages, facilitated by data from high-resource counterparts during the pretraining phase. However, it does indicate an interference effect, where high-resource languages may witness diminished performance — an issue partially mitigated by expanding the model capacity.

Language Clustering and Token Sharing

Moreover, the research offers insights into the emergent clustering behaviors in its shared token representation space. Languages with higher structural similarity tend to share representations, which implicitly aids in cross-lingual transfers.

Implications and Future Work

The results underscore the potential impact of deploying a single model across diverse linguistic landscapes, opening avenues for robust multilingual speech recognition systems. The findings carry significant implications for low-resource language processing, particularly in leveraging computational resources efficiently. Future research directions may include enhancing token sharing strategies to boost performance further, especially in languages with limited pretraining data.

The paper presents XLSR-53, a larger model covering 53 languages, which sets a new precedent for multilingual pretraining by demonstrating competitive results with reduced fine-tuning on several datasets. This reinforces the paradigm that increasing model capacity while maintaining a focus on efficient resource sharing across similar languages can further enhance system performance.

In conclusion, this paper contributes considerably to the sphere of multilingual speech recognition, offering an analytical basis for unsupervised cross-lingual representation learning. Its implications stretch both theoretical landscapes and practical applications, with the potential to inform future developments in the AI domain.