Editing State-of-the-art (section)

==== Wang, H., Wang, S., Zhang, W. Q., & Bai, J. (2023). Distilxlsr: A light weight cross-lingual speech representation model. ''arXiv preprint arXiv:2306.01303''. ====

*Summary: The authors introduce a compression scheme for multilingual self-supervised speech representation models aimed at enhancing speech recognition performance for low-resource languages while reducing model size for industrial applications. Experiments across two types of teacher models and 15 low-resource languages demonstrate that this method can reduce parameters by 50% while maintaining cross-lingual representation capabilities. The approach is shown to be generalizable across various languages and teacher models, with potential to improve the cross-lingual performance of English pretrained models. Key observations include the effectiveness of data splicing, the importance of layer-jumping initialization, the balance between model compression and performance, and underfitting challenges in low-resource scenarios.
* RQ: The paper investigates how to compress multilingual self-supervised speech representation models, specifically aiming to enhance speech recognition performance for low-resource languages while reducing the model size for easier industrial application.
* Hypothesis: It's possible to significantly reduce the size of multilingual speech representation models without substantially sacrificing performance across various languages by distilling cross-lingual models using only English data and applying techniques such as random phoneme shuffling, layer-jumping initialization, and data splicing.
* Conclusion: The proposed DistilXLSR model successfully reduces parameter size by 50% while maintaining cross-lingual representation capabilities across 15 low-resource languages. This model demonstrates its effectiveness through experimental results, showing comparable performance to larger teacher models and the potential for generalizability and improvement in cross-lingual performance of English pre-trained models.
* Critical Observations:
*# Randomly shuffling syllables within utterances to reduce linguistic information proved effective for distilling models with cross-lingual capabilities using only English data.
*# This novel method of initializing student models by leveraging teacher models' pre-trained weights across layers enhances the learning and representation ability of the distilled model.
*# The study highlights a trade-off between model size and performance, where the distilled models show only slight degradation in performance despite a significant reduction in size.
*# The paper acknowledges challenges like underfitting, especially evident in datasets with lower quality audio, suggesting that further research could explore structured pruning or other methods to mitigate this.
* Relevance: By employing a novel distillation approach that leverages English data, this model addresses the challenge of accessing and formatting training data across multiple languages, which is particularly difficult for low-resource languages. The effectiveness of DistilXLSR in maintaining performance across 15 low-resource languages, despite a substantial reduction in model size, showcases its potential in breaking down language barriers and enabling more equitable access to speech technology worldwide.