Summary of An Adapter-based Unified Model For Multiple Spoken Language Processing Tasks, by Varsha Suresh et al.
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks
by Varsha Suresh, Salah Aït-Mokhtar, Caroline Brun, Ioan Calapodescu
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach for developing a unified speech processing model capable of handling various downstream tasks, including Automatic Speech Recognition, Phoneme Recognition, Intent Classification, Slot Filling, and Spoken Emotion Recognition. By leveraging adapter-based fine-tuning, the authors demonstrate that a single encoder-decoder model can be trained to perform multiple tasks with an average improvement of 18.4% compared to baseline models while maintaining computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how self-supervised learning models can be fine-tuned for various speech processing tasks using adapter-based fine-tuning, achieving better results on the SUPERB benchmark than previous approaches. This innovative method could have significant implications for developing more efficient and effective speech processing systems in real-world applications. |
Keywords
» Artificial intelligence » Classification » Encoder decoder » Fine tuning » Self supervised
