Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable. This is why some initial effort have been reported on completely unsupervised speech recognition learned from unlabeled data only, although with relatively high error rates. In this paper, we develop a Generative Adversarial Network (GAN) to achieve this purpose, in which a Generator and a Discriminator learn from each other iteratively to improve the performance. We further use a set of Hidden Markov Models (HMMs) iteratively refined from the machine generated labels to work in harmony with the GAN. The initial experiments on TIMIT data set achieve an phone error rate of 33.1%, which is 8.5% lower than the previous state-of-the-art.

本研究使用生成对抗网络和隐马尔可夫模型开发了一种适用于低资源语言的无监督语音识别方法，并在 TIMIT 数据集上实现了33.1%的电话错误率，比现有技术提高了8.5%。

通过与迭代精化的隐马尔可夫模型协调的生成对抗网络实现完全无监督语音识别