Loading [a11y]/accessibility-menu.js
Unsupervised learning of acoustic features via deep canonical correlation analysis | IEEE Conference Publication | IEEE Xplore

Unsupervised learning of acoustic features via deep canonical correlation analysis


Abstract:

It has been previously shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning ac...Show More

Abstract:

It has been previously shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning acoustic features from this multi-view data with canonical correlation analysis (CCA). In contrast with previous work based on linear or kernel CCA, we use the recently proposed deep CCA, where the functional form of the feature mapping is a deep neural network. We apply the approach on a speaker-independent phonetic recognition task using data from the University of Wisconsin X-ray Microbeam Database. Using a tandem-style recognizer on this task, deep CCA features improve over earlier multi-view approaches as well as over articulatory inversion and typical neural network-based tandem features. We also present a new stochastic training approach for deep CCA, which produces both faster training and better-performing features.
Date of Conference: 19-24 April 2015
Date Added to IEEE Xplore: 06 August 2015
Electronic ISBN:978-1-4673-6997-8

ISSN Information:

Conference Location: South Brisbane, QLD, Australia

Contact IEEE to Subscribe

References

References is not available for this document.