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Automatic Speech Segmentation and Recognition using Class-Specific Features

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 113 - Number 17
Year of Publication: 2015
J. Ujwala Rekha
K. Shahu Chatrapati
A Vinaya Babu

Ujwala J Rekha, Shahu K Chatrapati and Vinaya A Babu. Article: Automatic Speech Segmentation and Recognition using Class-Specific Features. International Journal of Computer Applications 113(17):4-9, March 2015. Full text available. BibTeX

	author = {J. Ujwala Rekha and K. Shahu Chatrapati and A Vinaya Babu},
	title = {Article: Automatic Speech Segmentation and Recognition using Class-Specific Features},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {113},
	number = {17},
	pages = {4-9},
	month = {March},
	note = {Full text available}


The class-specific automatic speech recognition systems construct an individual classifier for each class based on its own feature set, wherein the feature set for each class is selected such that it distinguishes that class from the other classes most accurately. Consequently, different feature set sequences must be fed into each of the classifiers, and the output of each of the classifiers must be combined to predict the actual class of the observation sequences. However, speech is continuous, and to be able to apply class-specific features, speech should be segmented and fed to the classifiers, which requires the identification of segmentation cues. This paper proposes a framework that jointly segments, and combines the output of the class-specific classifiers in the absence of any segmentation cues using a recursive formulation.


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