Summary of Elp-adapters: Parameter Efficient Adapter Tuning For Various Speech Processing Tasks, by Nakamasa Inoue et al.
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks
by Nakamasa Inoue, Shinta Otake, Takumi Hirose, Masanari Ohi, Rei Kawakami
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces ELP-adapter tuning, a novel approach for self-supervised learning of generic representations from speech data. The method uses three types of adapters: encoder adapters (E-adapters), layer adapters (L-adapters), and a prompt adapter (P-adapter) to fine-tune transformer-based models for various speech processing tasks. The E-adapters help learn fine-grained speech representations, while L-adapters create paths to extract non-linguistic features for speaker verification and emotion recognition. The P-adapter appends pseudo features to CNN features for further improvement. The proposed method is evaluated across four downstream tasks using five backbone models, demonstrating its effectiveness with the WavLM backbone performing comparable or better than full fine-tuning on all tasks while requiring 90% fewer learnable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of learning from speech data without needing lots of information about each specific task. This method uses special adapters to help machines understand speech better and do different tasks like recognizing emotions or identifying speakers. The adapters work by adding extra information to the model’s understanding, making it more efficient. |
Keywords
» Artificial intelligence » Cnn » Encoder » Fine tuning » Prompt » Self supervised » Transformer
