Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching

ICLR 2019 Chih-Kuan YehJianshu ChenChengzhu YuDong Yu

We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a phoneme language model estimated from a non-overlapping corpus. We propose a fully unsupervised learning algorithm that alternates between solving two sub-problems: (i) learn a phoneme classifier for a given set of phoneme segmentation boundaries, and (ii) refining the phoneme boundaries based on a given classifier... (read more)

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