  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.
