MLMA: Towards Multilingual with Mamba Based Architectures
Abstract
Multilingual automatic speech recognition (ASR) remains a challenging task, especially when balancing performance across high- and low-resource languages. Recent advances in sequence modeling suggest that architectures beyond Transformers may offer better scalability and efficiency. In this work, we introduce MLMA (Multilingual Language Modeling with Mamba for ASR), a new approach that leverages the Mamba architecture -- an efficient state-space model optimized for long-context sequence processing -- for multilingual ASR. Using Mamba, MLMA implicitly incorporates language-aware conditioning and shared representations to support robust recognition across diverse languages. Experiments on standard multilingual benchmarks show that MLMA achieves competitive performance compared to Transformer-based architectures. These results highlight Mamba's potential as a strong backbone for scalable, efficient, and accurate multilingual speech recognition.
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 paper presents MLMA, a novel multilingual ASR framework based on the Mamba architecture, demonstrating its potential for efficient and scalable speech recognition across multiple languages. The technical contributions are significant, addressing the limitations of existing Transformer-based models and paving the way for more inclusive ASR systems.
Methodology
The paper introduces the MLMA framework, which leverages the Mamba architecture for multilingual ASR. The methodology is well-structured, integrating Mamba's state-space model with language-aware conditioning and shared representations. The use of ConMamba for feature extraction is a notable enhancement, allowing the model to capture both local and long-range dependencies effectively. However, the paper could benefit from a more detailed explanation of the training process and hyperparameter tuning.
Results & evaluation
The experiments are comprehensive, utilizing multiple large-scale multilingual datasets to validate the model's performance. The comparisons against Transformer-based architectures, particularly the Conformer model, are well-articulated, demonstrating MLMA's competitive edge in terms of WER and efficiency. The ablation studies provide valuable insights into the impact of model size and training data, although further exploration of different language pairs could enhance the robustness of the findings.
Reproducibility
The authors have made their code and weights publicly available, which is a positive aspect for reproducibility. However, the paper lacks detailed hyperparameter settings and specific training procedures, which could hinder replication efforts by other researchers.
Limitations
One limitation is the focus on European languages, which may not generalize to other linguistic families or low-resource languages. Additionally, while the results are promising, the paper does not address potential challenges in real-world applications, such as noise robustness or dialectal variations.
Broader impact
The MLMA framework has significant implications for the development of scalable ASR systems that can serve diverse linguistic communities, particularly in low-resource settings. Its efficiency and competitive performance could facilitate the deployment of ASR technologies in various applications, enhancing accessibility and communication across languages. The paper presents MLMA, a novel multilingual ASR framework based on the Mamba architecture, demonstrating its potential for efficient and scalable speech recognition across multiple languages. The technical contributions are significant, addressing the limitations of existing Transformer-based models and paving the way for more inclusive ASR systems.
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