Continual learning for automatic speech recognition (ASR) systems poses a challenge, especially with the need to avoid catastrophic forgetting while maintaining performance on previously learned tasks. This paper introduces a novel approach leveraging the machine speech chain framework to enable continual learning in ASR using gradient episodic memory (GEM). By incorporating a text-to-speech (TTS) component within the machine speech chain, we support the replay mechanism essential for GEM, allowing the ASR model to learn new tasks sequentially without significant performance degradation on earlier tasks. Our experiments, conducted on the LJ Speech dataset, demonstrate that our method outperforms traditional fine-tuning and multitask learning approaches, achieving a substantial error rate reduction while maintaining high performance across varying noise conditions. We showed the potential of our semi-supervised machine speech chain approach for effective and efficient continual learning in speech recognition.

该研究针对自动语音识别系统中的持续学习问题，提出了一种新颖的方法，利用机器语音链框架和梯度情节记忆（GEM）实现持续学习。研究表明，该方法在保持早期任务性能的同时，能够有效顺序学习新任务，并在各种噪声条件下显著降低错误率，展示出该半监督机器语音链方法在语音识别领域的有效性和效率。

使用梯度情节记忆的机器语音链持续学习