A Semisupervised Approach for Language Identification based on Ladder Networks

By Ehud Ben-Reuven, Jacob Goldberger. 2016

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In this study we address the problem of training a neuralnetwork for language identification using both labeled and unlabeled speech samples in the form of i-vectors. We propose a neural network architecture that can also handle out-of-set languages. We utilize a modified version of the recently proposed Ladder Network semisupervised training procedure that optimizes the reconstruction costs of a stack of denoising autoencoders. We show that this approach can be successfully applied to the case where the training dataset is composed of both labeled and unlabeled acoustic data. The results show enhanced language identification on the NIST 2015 language identification dataset.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “2 Financial Time Series Analysis Using Deep Learning Deep Learning (DL) techniques were successfully applied to a gamut of problems such as computer vision [16, 17], automatic speech recognition [18, 19], natural language processing [20, 21, 22], handwriting recognition [23], and bio-informatics [24”