This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert knowledge such as pronunciation dictionaries to build ASR systems, it still requires a large amount of paired data, i.e., speech utterances and their transcriptions. Cycle-consistency losses have been recently proposed as a way to mitigate the problem of limited paired data. These approaches compose a reverse operation with a given transformation, e.g., text-to-speech (TTS) with ASR, to build a loss that only requires unsupervised data, speech in this example. Applying cycle consistency to ASR models is not trivial since fundamental information, such as speaker traits, are lost in the intermediate text bottleneck. To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal. This is achieved by training a Text-To-Encoder model and defining a loss based on the encoder reconstruction error. Experimental results on the LibriSpeech corpus show that the proposed cycle-consistency training reduced the word error rate by 14.7% from an initial model trained with 100-hour paired data, using an additional 360 hours of audio data without transcriptions. We also investigate the use of text-only data mainly for language modeling to further improve the performance in the unpaired data training scenario.

该论文介绍了一种使用未配对数据训练端到端自动语音识别模型的方法，并提出了一种基于语音编码器状态序列而非原始信号的损失来解决在中间文本瓶颈中丢失基本信息的问题，通过对LibriSpeech语料库的实验结果表明，这种循环一致性训练可以将单词错误率从使用100小时配对数据训练的初始模型中减少14.7％，并通过研究仅使用文本数据进行语言建模进一步提高了未配对数据训练的性能。

端到端语音识别的循环一致性训练