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arXiv · 1708.04673

Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis

Qingming Tang·Weiran Wang·Karen Livescu
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Abstract

We study the problem of acoustic feature learning in the setting where we have access to another (non-acoustic) modality for feature learning but not at test time. We use deep variational canonical correlation analysis (VCCA), a recently proposed deep generative method for multi-view representation learning. We also extend VCCA with improved latent variable priors and with adversarial learning. Compared to other techniques for multi-view feature learning, VCCA's advantages include an intuitive latent variable interpretation and a variational lower bound objective that can be trained end-to-end efficiently. We compare VCCA and its extensions with previous feature learning methods on the University of Wisconsin X-ray Microbeam Database, and show that VCCA-based feature learning improves over previous methods for speaker-independent phonetic recognition.

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Qingming Tang, Weiran Wang, Karen Livescu. 2017-08-11. Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis. https://arxiv.org/abs/1708.04673

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