Summary of Skipformer: a Skip-and-recover Strategy For Efficient Speech Recognition, by Wenjing Zhu et al.
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition
by Wenjing Zhu, Sining Sun, Changhao Shan, Peng Fan, Qing Yang
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Skipformer architecture addresses a crucial issue in Automatic Speech Recognition (ASR) tasks by introducing a “Skip-and-Recover” Conformer-based attention model that dynamically reduces the input sequence length while maintaining recognition accuracy. The new approach, which builds upon conformer-based models, utilizes an intermediate CTC output to split frames into three groups: crucial, skipping, and ignoring. This allows for efficient processing of long input sequences, achieving a 31x reduction on Aishell-1 and 22x reduction on Librispeech corpus. The Skipformer model outperforms recent baseline models in terms of recognition accuracy and inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automatic Speech Recognition is an important technology that helps computers understand human speech. Right now, the best way to do this uses a type of model called a Conformer. But these models have a problem – they get overwhelmed when dealing with very long speeches. To fix this, researchers developed a new model called Skipformer. It’s like a special filter that helps sort out which parts of the speech are most important. By doing this, Skipformer can process longer speeches more efficiently and accurately than other models. This is an exciting breakthrough that could help computers better understand human language. |
Keywords
* Artificial intelligence * Attention * Inference
