Summary of Continual Learning in Machine Speech Chain Using Gradient Episodic Memory, by Geoffrey Tyndall et al.
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory
by Geoffrey Tyndall, Kurniawati Azizah, Dipta Tanaya, Ayu Purwarianti, Dessi Puji Lestari, Sakriani Sakti
First submitted to arxiv on: 27 Nov 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 proposes a novel approach to enable continual learning in automatic speech recognition (ASR) systems, allowing them to learn new tasks sequentially without forgetting previous ones. The method leverages the machine speech chain framework and gradient episodic memory (GEM), incorporating a text-to-speech (TTS) component to support replay mechanisms. Experiments on the LJ Speech dataset demonstrate that this approach outperforms traditional fine-tuning and multitask learning methods, achieving significant error rate reductions while maintaining high performance across varying noise conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines get better at recognizing speech by teaching them new skills without forgetting old ones. The idea is to use a special framework called the machine speech chain, which includes a way for machines to “remember” what they’ve learned before. This lets the machine learn new tasks one after another without losing its ability to recognize speech correctly. The researchers tested this method on some speech data and found that it works better than other approaches at learning new skills while keeping old ones. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning
