Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2104.01027v2)

Abstract: Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at https://github.com/pytorch/fairseq.

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

The paper "Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training" provides a comprehensive investigation into the impact of domain shift on self-supervised learning in the context of automatic speech recognition (ASR). The authors explore scenarios where the domains of unlabeled data for pre-training differ from those used for fine-tuning and testing, addressing a less-studied but practically significant aspect of self-supervised learning.

Key Contributions

1. Domain Shift Impact: The paper systematically examines various configurations of domain mismatches in the self-supervised ASR pipeline. It demonstrates that introducing unlabeled data from the target domain during pre-training significantly enhances performance.
2. In-Domain Pre-Training Benefits: The authors find that pre-training on unlabeled data similar to the test domain reduces the performance gap between models trained on domain-matched and unmatched labeled data by 66%-73%. Such findings underscore the importance of domain adaptation in enhancing model performance without costly labeled data.
3. Multiple Domain Pre-Training: Pre-training across multiple domains improves model generalization to unseen domains. This suggests a strategic approach in using diverse datasets to produce more robust models against domain variation.
4. Large-Scale Performance: The paper details experiments with larger pre-trained models and datasets, showing that pre-training with additional in-domain data remains effective even when more labeled data becomes available. The robustness of these models to unseen domains further underscores their practical utility.
5. Comparative Analysis: The work conducts extensive evaluations against competitive baselines and demonstrates improvements in both in-domain and out-of-domain scenarios, especially when constraints on labeled data exist.

Experimental Insights

The experiments conducted with various configurations of pre-training and fine-tuning data provide detailed insights into the relationship between domain similarity and model performance. Carefully structured experiments show that increasing the amount of in-domain pre-training data consistently improves ASR performance, affirming the hypothesis of domain-specific adaptation benefits.

Additionally, the robustness of models subjected to multiple domains during pre-training suggests that a diversified pre-training dataset can serve as a buffer against domain mismatches encountered during deployment.

Implications and Future Directions

The findings from this paper have important implications for the deployment of ASR systems in real-world applications, where domain-specific labeled data can be scarce. By capitalizing on abundant unlabeled data, practitioners can substantially improve ASR performance in their target domain without incurring high annotation costs.

Future research may delve into optimizing the balance between in-domain and out-of-domain data during pre-training. Further exploration could also include the investigation of self-supervised learning techniques across other modalities and languages, potentially extending these findings into broader AI applications.

The paper's contribution extends the understanding of domain shift in self-supervised learning, highlighting strategic pathways to leverage unlabeled data effectively. As the field continues to evolve, the methodologies and insights presented will likely influence future developments in robust AI systems adaptable to diverse and dynamic environments.