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.

本文介绍了 SpeeChain，一种开源的基于 Pytorch 的工具包，用于开发用于大规模使用的机器语音链。该工具包着重于 TTS 到 ASR 链这一核心组件，旨在通过未经口头语言的文本来增强 TTS 数据，从而增强机器语音链。研究者们通过实现易于使用的多 GPU 批级模型推断、多数据加载器批量生成和新颖的数据选择技术来构建大规模 TTS-to-ASR 链的高效流水线。本文首先解释了 TTS-to-ASR 链的总体流程及其各个步骤的困难之处。然后，研究者们对不同类型的未标记数据、数据过滤阈值、批级组成以及真实-合成数据比率进行了详细的剖析研究。最后，通过在 LibriSpeech 的 train_clean_460 上进行实验，证明了半监督学习机制下，TTS-to-ASR 极大地提高了 WER。

SpeeChain：用于大规模机器语音链的语音工具包