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arXiv · 1706.02124

Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks

Marian Tietz·Tayfun Alpay·Johannes Twiefel·Stefan Wermter
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Abstract

Ladder networks are a notable new concept in the field of semi-supervised learning by showing state-of-the-art results in image recognition tasks while being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the model is able to consistently outperform the baseline and achieve fully-supervised baseline performance with only 75% of all labels which demonstrates that the model is capable of using unsupervised data as an effective regulariser.

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Marian Tietz, Tayfun Alpay, Johannes Twiefel, Stefan Wermter. 2017-09-18. Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks. https://doi.org/10.1007/978-3-319-68600-4_1

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