HYBRID STATISTICAL STATIC AND DYNAMIC PRONUNCIATION MODELS DESIGNED TO BE TRAINED BY A MEDIUM-SIZE CORPUS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

VAZIRNEZHAD B.; ALMASGANJ F.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING Year:2007 | Volume:5 | Issue:1 (A) Page(s): 28-40

Download Full-Text

Persian Verion

View:

744

Download:

Cites:

Information Journal Paper

Title

HYBRID STATISTICAL STATIC AND DYNAMIC PRONUNCIATION MODELS DESIGNED TO BE TRAINED BY A MEDIUM-SIZE CORPUS

Author(s)

VAZIRNEZHAD B. | ALMASGANJ F. | Issue Writer Certificate

Keywords

PRONUNCIATION MODELSQ4

CONTINUOUS SPEECH RECOGNITIONQ4

LEXICONQ4

Abstract

Generating pronunciation variants of words is an important applicable subject in speech research and is used extensively in automatic speech recognition and segmentation systems. In this way, decision trees are extensively used to model pronunciation variants of words and sub-word units. In case of word units and very large vocabulary, in order to train necessary decision trees, a huge amount of speech utterances that contain all of the needed words in vocabulary with a sufficient number of repetitions for each one is required; additionally an extra corpus is needed for every word Which is not included in the original training corpus and may be added to the vocabulary In the future. To solve these drawbacks, we have designed generalized decision trees, which can be trained using a medium-size corpus over group’s similar words to share information on pronunciation, instead of training a separate tree for ever single word. Generalized decision trees predict places in word where substitution, deletion and insertion phonemes may occur. Next to this step, In order to specifically determine word variants appropriate statistical contextual rules are applied to the permitted places. The hybrids of generalized decision trees and contextual rules are designed in static and dynamic versions. The hybrid static PRONUNCIATION MODELS take into account word phonological structures, unigram probabilities, stress and phone context information simultaneously, while the hybrid dynamic models consider an extra feature. Speaking rate 10 generate pronunciation variants 0f words. Using the word variants, generated by static and dynamic models, in the LEXICON of SHENAVA Persian continuous speech recognizer, relative word error raye reductions of as high as 8.1% and 10.3% are obtained respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

VAZIRNEZHAD, B., & ALMASGANJ, F.. (2007). HYBRID STATISTICAL STATIC AND DYNAMIC PRONUNCIATION MODELS DESIGNED TO BE TRAINED BY A MEDIUM-SIZE CORPUS. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, 5(1 (A)), 28-40. SID. https://sid.ir/paper/70645/en

Vancouver: Copy

VAZIRNEZHAD B., ALMASGANJ F.. HYBRID STATISTICAL STATIC AND DYNAMIC PRONUNCIATION MODELS DESIGNED TO BE TRAINED BY A MEDIUM-SIZE CORPUS. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING[Internet]. 2007;5(1 (A)):28-40. Available from: https://sid.ir/paper/70645/en

IEEE: Copy

B. VAZIRNEZHAD, and F. ALMASGANJ, “HYBRID STATISTICAL STATIC AND DYNAMIC PRONUNCIATION MODELS DESIGNED TO BE TRAINED BY A MEDIUM-SIZE CORPUS,” THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, vol. 5, no. 1 (A), pp. 28–40, 2007, [Online]. Available: https://sid.ir/paper/70645/en

Related Journal Papers