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Producing a large annotated speech corpus for training ASR systems remainsdifficult for more than 95% of languages all over the world which arelow-resourced, but collecting a relatively big unlabeled data set for suchlanguages is more achievable. This is why some initial effort have beenreported on completely unsupervised speech recognition learned from unlabeleddata only, although with relatively high error rates. In this paper, we developa Generative Adversarial Network (GAN) to achieve this purpose, in which aGenerator and a Discriminator learn from each other iteratively to improve theperformance. We further use a set of Hidden Markov Models (HMMs) iterativelyrefined from the machine generated labels to work in harmony with the GAN. Theinitial 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.

Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models