NDLI: Contrastive auto-encoder for phoneme recognition

Content Provider	IEEE Xplore Digital Library
Author	Xin Zheng Zhiyong Wu Meng, H. Lianhong Cai
Copyright Year	2014
Description	Author affiliation: Tsinghua-CUHK Joint Res. Center for Media Sci., Tsinghua Univ., Shenzhen, China (Xin Zheng; Zhiyong Wu; Meng, H.; Lianhong Cai)
Abstract	Speech data typically contains task irrelevant information lying within features. Specifically, phonetic information, speaker characteristic information, emotional information and noise are always mixed together and tend to impair one another for certain task. We propose a new type of auto-encoder for feature learning called contrastive auto-encoder. Unlike other variants of auto-encoders, contrastive auto-encoder is able to leverage class labels in constructing its representation layer. We achieve this by modeling two autoencoders together and making their differences contribute to the total loss function. The transformation built with contrastive auto-encoder can be seen as a task-specific and invariant feature learner. Our experiments on TIMIT clearly show the superiority of the feature extracted from contrastive auto-encoder over original acoustic feature, feature extracted from deep auto-encoder, and feature extracted from a model that contrastive auto-encoder originates from.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	2529
Ending Page	2533
File Size	120275
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Training Hidden Markov models Neurons DNA Data models Acoustics restricted Boltzmann machine (RBM) auto-encoder contrastive auto-encoder phoneme recognition deep neural network (DNN)
Content Type	Text
Resource Type	Article

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