Summary of Efficient Adapter Finetuning For Tail Languages in Streaming Multilingual Asr, by Junwen Bai et al.
Efficient Adapter Finetuning for Tail Languages in Streaming Multilingual ASR
by Junwen Bai, Bo Li, Qiujia Li, Tara N. Sainath, Trevor Strohman
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Language-Dependent Adapter (LDA) finetuning method is designed to improve the performance of end-to-end Automatic Speech Recognition (ASR) models in multilingual scenarios. By leveraging pre-trained speech models and a cascaded Conformer transducer framework, the approach aims to reduce the impact of heterogeneous language data and imbalanced distributions on model performance. The LDA adapter, which accounts for only 0.4% of the full model per language, is plugged into the frozen foundation model and trained using noisy student training. The model’s performance is validated on a challenging multilingual dictation dataset, featuring 39 tail languages across various scripts. The results show an average 12.2% word error rate reduction and up to 37.5% improvement on a single locale compared to existing methods. This parameter-efficient approach can match the quality of full model finetuning, alleviating the asynchronous peak performance issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new method for improving automatic speech recognition in many languages at once. They wanted to make it easier to deploy and train these models by using powerful pre-trained models as a starting point. However, they faced challenges due to differences between languages and imbalanced data availability. To solve this issue, they proposed the Language-Dependent Adapter (LDA) finetuning method, which is simple yet effective. This approach can reduce word error rates by up to 37.5% for specific languages and is much more efficient than previous methods. |
Keywords
* Artificial intelligence * Parameter efficient
