NDLI: A hybrid statistical model to generate pronunciation variants of words

Content Provider	IEEE Xplore Digital Library
Author	Vazirnezhad, B. Almasganj, F. Bijankhan, M.
Copyright Year	2005
Description	Author affiliation: Biomed. Eng. Fac., Amirkabir Univ. of Technol., Tehran, Iran (Vazirnezhad, B.)
Abstract	Generating pronunciation variants of words is an important applicable subject in speech researches and is used extensively in automatic speech segmentation and recognition systems. In this way, decision trees are extremely used to model pronunciation variants of words and sub-word unites. In the case of word unites and very large vocabulary, to train necessary decision trees we need a huge amount of speech utterances which contains all of the needed words with a sufficient number of each one. This approach besides demanding very large data, for new words needs some new extra corpus. To solve these problems we have used generalized decision trees, that each tree is trained for a group of words with similar phonemic structure instead of a single word. These trees can predict regions of the words in which substitution, deletion and insertion of phonemes would occur. Next to this step, appropriate statistical contextual rules, which are extracted from a large speech corpus, is applied to these regions in order to generate words variants. This new hybrid d-tree/c-rule approach takes into account word phonological structures, stress, and phone context information simultaneously and an ordinary size speech corpus is sufficient to train its models. By using the word variants obtained by this method in the lexicon of "SHENAVA", a Persian ACSR, a relative WER% reduction of as high as 6% was obtained.
Starting Page	106
Ending Page	110
File Size	1273785
Page Count	5
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598716
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Hybrid power systems Decision trees Data mining Speech processing Stress Automatic speech recognition Context modeling Biomedical engineering Biomedical signal processing
Content Type	Text
Resource Type	Article

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