NDLI: Improved clustered hierarchical tandem system with bottom-up processing

Content Provider	IEEE Xplore Digital Library
Author	Shuo-Yiin Chang Lin-Shan Lee
Copyright Year	2009
Description	Author affiliation: Graduate Institute of Communication Engineering, National Taiwan University, Taiwan, Republic of China (Shuo-Yiin Chang; Lin-Shan Lee)
Abstract	The outputs of multi-layer perceptron (MLP) classifiers have been successfully used in tandem systems as features for HMM-based automatic speech recognition. In a previous paper, we proposed Data-driven Clustered Hierarchical MLP (CHMLP) tandem system yielding improved performance by dividing the complicated global phone classification problem into simpler hierarchical tasks, in which specialized MLPs are trained to classify small clusters of confusing phones in a hierarchical structure. In this paper a bottom-up processing is further proposed to enhance the classification in the above CHMLP and offer even better performance. MLP rescoring for the tandem system is also investigated. The best result achieved 19.1% relative error reduction over the MFCC baseline.
Starting Page	4441
Ending Page	4444
File Size	118042
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4960615
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Automatic speech recognition Hidden Markov models Mel frequency cepstral coefficient Clustering algorithms Multilayer perceptrons Neural networks Lattices Feature extraction LVCSR Neural Network Tandem system
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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