Summary of Maximizing Data Efficiency For Cross-lingual Tts Adaptation by Self-supervised Representation Mixing and Embedding Initialization, By Wei-ping Huang et al.
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization
by Wei-Ping Huang, Sung-Feng Huang, Hung-yi Lee
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 transfer learning framework for text-to-speech systems achieves effective language adaptation using minimal labeled and unlabeled data. By leveraging self-supervised features during pretraining and replacing noisy pseudo labels with these features during fine-tuning, the method can utilize more information from unlabeled data than conventional approaches. Experimental results show that the framework synthesizes intelligible speech in unseen languages with only 4 utterances of labeled data and 15 minutes of unlabeled data, surpassing conventional techniques even when more data is available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers talk in different languages uses very little data from those languages. Usually, these systems need a lot of data to work well. But this new method works with just a few examples and some extra information that isn’t labeled as part of the language. It’s like using clues to figure out how to say words correctly. The results are impressive: it can make speech sound natural in languages it hasn’t seen before, even with just a little data. |
Keywords
* Artificial intelligence * Fine tuning * Pretraining * Self supervised * Transfer learning
