NDLI: Random-forests-based phonetic decision trees for conversational speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Jian Xue Yunxin Zhao
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO (Jian Xue; Yunxin Zhao)
Abstract	In this paper we present a novel technique of constructing phonetic decision trees (PDTs) for acoustic modeling in conversational speech recognition. We use random forests (RF) to train a set of PDTs for each phone-state unit and obtain multiple acoustic models accordingly, and we extend the PDT-based state tying to RF-based state-tying. We combine acoustic scores at the model level in decoding search. Several methods are investigated to estimate the weight parameters for model combination, including maximum likelihood estimation of the weights from training data, as well as using confidence scores of P-value or relative entropy to obtain the weights dynamically from online data. Experimental results on a telemedicine automatic captioning task demonstrate that the proposed RF-PDT technique leads to significant improvements in word recognition accuracy.
Starting Page	4169
Ending Page	4172
File Size	169978
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Speech recognition Radio frequency Training data Bagging Classification tree analysis Maximum likelihood decoding Sampling methods Voting Computer science score combination Random Forests phonetic decision trees acoustic modeling
Content Type	Text
Resource Type	Article

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