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We consider the problem of training speech recognition systems without usingany labeled data, under the assumption that the learner can only access to theinput utterances and a phoneme language model estimated from a non-overlappingcorpus. We propose a fully unsupervised learning algorithm that alternatesbetween solving two sub-problems: (i) learn a phoneme classifier for a givenset of phoneme segmentation boundaries, and (ii) refining the phonemeboundaries based on a given classifier. To solve the first sub-problem, weintroduce a novel unsupervised cost function named Segmental Empirical OutputDistribution Matching, which generalizes the work in (Liu et al., 2017) tosegmental structures. For the second sub-problem, we develop an approximate MAPapproach to refining the boundaries obtained from Wang et al. (2017).Experimental results on TIMIT dataset demonstrate the success of this fullyunsupervised phoneme recognition system, which achieves a phone error rate(PER) of 41.6%. Although it is still far away from the state-of-the-artsupervised systems, we show that with oracle boundaries and matching languagemodel, the PER could be improved to 32.5%.This performance approaches thesupervised system of the same model architecture, demonstrating the greatpotential of the proposed method.

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching