Lamer-SSL: Layer-aware Mixture of LoRA Experts for Continual Multilingual Expansion of Self-supervised Models without Forgetting
Abstract
Despite their impressive performance, self-supervised speech models often struggle to generalize to new languages and tend to forget previously acquired knowledge during continual training. To address this, we propose Lamer-SSL, a parameter-efficient framework that integrates a Layer-Aware MixturE of LoRA Experts (Lamer) module with a replay strategy. The Lamer module enables flexible balancing between shared and language-specific representations, while layer-aware expert allocation assigns more experts to deeper layers where semantic information is richer. Meanwhile, the replay strategy retains prior knowledge using minimal data, mitigating forgetting during continual training. Experiments on automatic speech recognition (ASR) and language identification (LID) demonstrate that Lamer-SSL extends self-supervised models to new languages effectively while maintaining strong performance on previously learned languages with only 2.14% parameters being trainable.
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 Lamer-SSL, a parameter-efficient framework that effectively extends self-supervised speech models to new languages while mitigating forgetting, thereby enhancing multilingual capabilities in speech processing. This work represents a notable step forward in the field, combining innovative methodologies with practical applications in multilingual environments.
Methodology
The proposed Lamer-SSL framework integrates a novel layer-aware Mixture of LoRA Experts with a replay strategy, which is a significant advancement in the continual multilingual adaptation of self-supervised speech models. The methodology effectively addresses the challenges of catastrophic forgetting and language interference by dynamically allocating experts based on the hierarchical nature of SSL representations. This approach is well-grounded in existing literature, yet it introduces a unique combination of techniques that enhance parameter efficiency and model performance.
Results & evaluation
The experimental setup is robust, utilizing established datasets for automatic speech recognition and language identification tasks. The results demonstrate clear performance improvements over baseline models, including mHuBERT and HuBERT-LORA, showcasing the effectiveness of the proposed method. The inclusion of an ablation study further strengthens the findings by isolating the contributions of each component of the Lamer-SSL framework.
Reproducibility
The paper provides sufficient details regarding the datasets, training configurations, and evaluation protocols, which are essential for reproducibility. However, the absence of a publicly available code repository or demo URL limits the practical reproducibility of the results.
Limitations
One notable limitation is the reliance on a specific architecture (HuBERT) as the base model, which may restrict the generalizability of the findings to other architectures. Additionally, while the replay strategy is effective, its dependence on a limited subset of prior data may not fully capture the diversity of previous languages learned.
Broader impact
The advancements presented in Lamer-SSL have significant implications for multilingual speech processing, particularly in low-resource language scenarios. By enabling efficient continual learning, this work could facilitate the development of more inclusive speech technologies that better serve diverse linguistic populations. The main contribution of this paper is the introduction of Lamer-SSL, a parameter-efficient framework that effectively extends self-supervised speech models to new languages while mitigating forgetting, thereby enhancing multilingual capabilities in speech processing. This work represents a notable step forward in the field, combining innovative methodologies with practical applications in multilingual environments.
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