Sequence-to-sequence ASR models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for self-supervised and supervised training in such models. This work builds upon recent results showing notable improvements in self-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with text-to-speech (TTS) models. In particular, this work proposes a new self-supervised loss combining an end-to-end differentiable ASR$\rightarrow$TTS loss with a point estimate TTS$\rightarrow$ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of \%WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided to reproduce the experiments.

本文提出了一种新的半监督训练方法，该方法结合了端到端的不可区分的ASR->TTS损失和TTS->ASR损失，利用未配对的语音和文本数据，相较于其他相关技术在WSJ和Librispeech语料库上的WER性能更好。

使用非成对语音和文本的半监督序列到序列自动语音识别