Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/4186
Title: A HIERARCHICAL APPROACH IN TAMIL PHONEME CLASSIFICATION USING SUPPORT VECTOR MACHINE
Authors: Karpagavalli, S
Chandra, E
Keywords: Hierarchial Classification Mel-Frequency Cepstral Coefficients
Spectral Transition Measure
Issue Date: 2015
Publisher: Indian Journal of Science and Technology
Abstract: Most of the speech recognition systems are designed based on the sub-word unit phoneme which is the basic sound unit of a language. In the proposed work, a novel hierarchical approach based phoneme classification task has been carried out to reduce time complexity and search space. Hierarchical classification of set of Tamil phonemes has been done in three levels. Phoneme boundaries of the given speech utterance are identified using Spectral Transition Measure (STM) and phonemes are separated. Mel-Frequency Cepstral Coefficients (MFCC) are extracted for each phoneme represented by 9 frames including the contextual frames of corresponding phoneme. In each hierarchical level, different number of models is built using Support Vector Machine (SVM) for classifying each phoneme group/phoneme. It is observed from the results that in hierarchical approach phoneme group recognition rate at level 1 and 2 has greatly improved compared to flat classification model. Complexity of search space is significantly reduced at level 2 and level 3 contrasts to flat phoneme classification model. Hierarchical phoneme classifier can be very well employed in phoneme recognition task which is useful in applications such as spoken term detection, out-ofvocabulary detection, named entity recognition, spoken document retrieval.
URI: https://indjst.org/articles/a-hierarchical-approach-in-tamil-phoneme-classification-using-support-vector-machine
ISSN: 0974-5645
Appears in Collections:2.Article (30)

Files in This Item:
File Description SizeFormat 
A HIERARCHICAL APPROACH IN TAMIL PHONEME CLASSIFICATION USING SUPPORT VECTOR MACHINE.pdf412.87 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.