The paper combines machine speech chain text-to-speech replay with gradient episodic memory to let an ASR model learn a noisy speech task without forgetting clean speech, reporting a 40% average CER reduction over fine-tuning on LJ Speech.
SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chain
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abstract
This paper introduces SpeeChain, an open-source Pytorch-based toolkit designed to develop the machine speech chain for large-scale use. This first release focuses on the TTS-to-ASR chain, a core component of the machine speech chain, that refers to the TTS data augmentation by unspoken text for ASR. To build an efficient pipeline for the large-scale TTS-to-ASR chain, we implement easy-to-use multi-GPU batch-level model inference, multi-dataloader batch generation, and on-the-fly data selection techniques. In this paper, we first explain the overall procedure of the TTS-to-ASR chain and the difficulties of each step. Then, we present a detailed ablation study on different types of unlabeled data, data filtering thresholds, batch composition, and real-synthetic data ratios. Our experimental results on train_clean_460 of LibriSpeech demonstrate that our TTS-to-ASR chain can significantly improve WER in a semi-supervised setting.
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cs.CL 1years
2024 1verdicts
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Continual Learning in Machine Speech Chain Using Gradient Episodic Memory
The paper combines machine speech chain text-to-speech replay with gradient episodic memory to let an ASR model learn a noisy speech task without forgetting clean speech, reporting a 40% average CER reduction over fine-tuning on LJ Speech.