Summary of Improving Self-supervised Pre-training Using Accent-specific Codebooks, by Darshan Prabhu et al.
Improving Self-supervised Pre-training using Accent-Specific Codebooks
by Darshan Prabhu, Abhishek Gupta, Omkar Nitsure, Preethi Jyothi, Sriram Ganapathy
First submitted to arxiv on: 4 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 The proposed accent-aware adaptation technique for self-supervised learning introduces trainable accent-specific codebooks to the architecture, enabling the model to capture accent information during pre-training. This approach outperforms other accent-adaptation methods on both seen and unseen English accents on the Mozilla Common Voice dataset, achieving up to 9% relative reduction in word error rate (WER). The technique leverages self-supervised learning and pre-training of Automatic Speech Recognition (ASR) models for improved accent invariance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to improve automatic speech recognition systems when they encounter different accents. Even with advanced training, these systems often struggle to recognize words spoken with different accents. The team behind this work created a new technique that uses special codebooks specifically designed to capture the features of each accent. They tested their approach on a large dataset and found that it significantly outperformed other methods, reducing errors by up to 9%. This could have important implications for how we use speech recognition technology in real-life applications. |
Keywords
* Artificial intelligence * Self supervised
