Summary of Linear-complexity Self-supervised Learning For Speech Processing, by Shucong Zhang et al.
Linear-Complexity Self-Supervised Learning for Speech Processing
by Shucong Zhang, Titouan Parcollet, Rogier van Dalen, Sourav Bhattacharya
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper explores the application of a linear-complexity context encoder for self-supervised learning (SSL) in speech processing tasks. Current SSL models require extensive pre-training with high-end GPUs, making them impractical for real-world applications. The proposed SummaryMixing model, which outperforms traditional multi-headed self-attention (MHSA) in supervised training, is adapted for SSL and demonstrates equivalent or better performance on the MP3S benchmark. This results in a significant reduction of pre-training time and peak VRAM requirements, enabling the training of larger models within a reasonable timeframe. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to train language models using less computer power and time. Language models need to be trained by processing large amounts of audio data, which takes a lot of time and computing resources. The authors propose a more efficient way to process this data that still produces good results. This could make it possible to train larger and more accurate language models in the future. |
Keywords
» Artificial intelligence » Encoder » Self attention » Self supervised » Supervised
