Summary of Towards Robust Speech Representation Learning For Thousands Of Languages, by William Chen et al.
Towards Robust Speech Representation Learning for Thousands of Languages
by William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper proposes XEUS, a novel Cross-lingual Encoder for Universal Speech, designed to extend speech technologies to more languages by leveraging self-supervised learning (SSL). The model is trained on over 1 million hours of data across 4057 languages, quadrupling the language coverage of existing SSL models. To address the diversity of multilingual speech data, the authors introduce a novel dereverberation objective and augment the typical SSL masked prediction approach. XEUS outperforms or achieves comparable results to state-of-the-art (SOTA) SSL models on various tasks, setting a new SOTA on the ML-SUPERB benchmark with an improvement of 0.8% and 4.4% over MMS 1B and w2v-BERT 2.0 v2, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to help speech technologies understand more languages without needing labeled data. They train a special model called XEUS on huge amounts of audio recordings from 4057 different languages. This helps them extend their language coverage by four times! To make sure the model works well with different kinds of audio, they add an extra step that helps remove background noise. The results show that XEUS is better than other models at understanding speech in many tasks. |
Keywords
» Artificial intelligence » Bert » Encoder » Self supervised
