MiLorE-SSL: Scaling Multilingual Capabilities in Self-Supervised Models without Forgetting
Abstract
Self-supervised learning (SSL) has greatly advanced speech representation learning, but multilingual SSL models remain constrained to languages encountered during pretraining. Retraining from scratch to incorporate new languages is computationally expensive, while sequential training without migitation strategies often leads to catastrophic forgetting. To address this, we propose MiLorE-SSL, a lightweight framework that combines LoRA modules with a soft mixture-of-experts (MoE) mechanism for efficient continual multilingual training. LoRA provides efficient low-rank adaptation, while soft MoE promotes flexible expert sharing across languages, reducing cross-lingual interference. To further mitigate forgetting, we introduce limited replay data from existing languages, avoiding reliance on large historical corpora. Experiments on ML-SUPERB demonstrate that MiLorE-SSL achieves strong performance in new languages and improves the ability in existing ones with only 2.14% trainable parameters.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of MiLorE-SSL, a novel framework that effectively combines LoRA and soft MoE for continual multilingual training in self-supervised speech models. This work addresses critical challenges in multilingual SSL, demonstrating significant improvements in performance while maintaining efficiency and scalability.
Methodology
The proposed MiLorE-SSL framework innovatively combines LoRA and soft mixture-of-experts (MoE) mechanisms to address the challenges of multilingual self-supervised learning. The architecture is well-structured, leveraging frozen components to preserve existing knowledge while allowing for efficient adaptation to new languages. The integration of a replay strategy to mitigate catastrophic forgetting is a thoughtful addition, enhancing the model's ability to retain previously learned information without requiring extensive historical data. The methodology is sound and demonstrates a clear understanding of existing challenges in multilingual SSL.
Results & evaluation
The experiments conducted on the ML-SUPERB benchmark provide a robust evaluation of the proposed method. The results indicate significant improvements in performance across multiple languages and tasks, showcasing the effectiveness of MiLorE-SSL in both ASR and language identification. The comparison with state-of-the-art models like mHuBERT-147 and HuBERT-Large further validates the contributions of the proposed framework. However, the paper could benefit from additional ablation studies to further dissect the contributions of individual components within the MiLorE-SSL architecture.
Reproducibility
The paper outlines the training configurations and datasets used, which aids in reproducibility. However, the absence of a publicly available code repository or demo limits the ability for other researchers to replicate the results fully. Providing access to the model and training scripts would significantly enhance reproducibility and allow for further exploration by the community.
Limitations
While the proposed method shows promise, it is limited by the reliance on a small subset of replay data, which may not capture the full diversity of previously learned languages. Additionally, the performance on low-resource languages compared to high-resource languages could be further explored. The model's scalability with an increasing number of languages and the potential for increased cross-lingual interference remain areas for further investigation.
Broader impact
The advancements presented in this paper have significant implications for multilingual speech processing, particularly in low-resource language contexts. By enabling efficient continual learning without catastrophic forgetting, MiLorE-SSL could facilitate the development of more inclusive and accessible speech technologies. The framework's potential applications extend to various domains, including automatic speech recognition, translation, and human-computer interaction, making it a valuable contribution to the field. The main contribution of this paper is the introduction of MiLorE-SSL, a novel framework that effectively combines LoRA and soft MoE for continual multilingual training in self-supervised speech models. This work addresses critical challenges in multilingual SSL, demonstrating significant improvements in performance while maintaining efficiency and scalability.
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