While the SFM+LLM architecture presents a promising approach for speech-to-text tasks, several limitations need to be addressed:
Dependency on SFM Quality: The study emphasizes that the choice of SFM is paramount, which can lead to challenges in scenarios where high-quality SFMs are not available for certain languages or dialects. Future research should focus on developing more robust SFMs that can generalize better across diverse languages and acoustic conditions.
Adapter Complexity: The varying performance of different adapters suggests that designing an optimal adapter can be complex and task-dependent. Future work could explore automated or adaptive methods for selecting and tuning adapters based on the specific characteristics of the input data, potentially using meta-learning techniques.
Computational Efficiency: The integration of SFMs and LLMs can be computationally intensive, especially for real-time applications. Research should aim to optimize the architecture for efficiency, possibly through model distillation or pruning techniques that reduce the size and complexity of the models while maintaining performance.
Limited Language Coverage: The current models may not cover all languages equally well, particularly low-resource languages. Future research should prioritize the development of multilingual SFMs that can effectively handle a wider range of languages, potentially through transfer learning from high-resource to low-resource languages.
Evaluation Metrics: The reliance on specific evaluation metrics like COMET and WER may not capture all aspects of model performance, particularly in real-world applications. Future studies should consider a broader set of evaluation criteria that reflect user experience and practical usability.