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Experiments on front-end techniques and segmentation model for robust Indian Language speech recognizer
Published in IEEE Computer Society
2014
Abstract
Recent contributions in the area of Automatic Speech Recognition (ASR) for Indian Languages has been increased. This paper serves as a comprehensive study of different feature extraction methods namely MFCC, PLP, RASTA-PLP and PNCC. An attempt to find out which of these front end techniques performs better for real world Indian Language data is analyzed experimentally. Then, an isolated word recognizer is built for three Indian languages (i.e., Tamil, Assamese and Bengali) under real world conditions and investigates the importance of handling long silence using segmentation method. The experimental analysis shows that PNCC provides better performance for clean data whereas MFCC shows improved performance in case of multi-condition speech data. © 2014 IEEE.
About the journal
JournalData powered by Typeset2014 20th National Conference on Communications, NCC 2014
PublisherData powered by TypesetIEEE Computer Society
Open AccessNo
Concepts (11)
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    Feature extraction
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    Image segmentation
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    Automatic speech recognition
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    COMPARISON OF FRONT-END TECHNIQUES
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    Experimental analysis
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    FEATURE EXTRACTION METHODS
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    NOISE ROBUSTNESS
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    SEGMENTATION METHODS
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    Segmentation models
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    SILENCE HANDLING
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    Speech recognition