TY - GEN
T1 - A comparative study of discriminative approaches for classifying languages into tonal and non-tonal categories at syllabic level
AU - Choudhury, Biplav
AU - Bhanja, Chuya China
AU - Choudhury, Tameem S.
AU - Laskar, R. H.
AU - Pramanik, Aniket
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/27
Y1 - 2016/10/27
N2 - Languages spoken by us, on the basis of how they use tone to convey a meaning, can be grouped into two categories: Tonal and Non-Tonal languages. Pitch is used as a figure of speech in the case of tonal languages. The connotation of a word changes depending on the pitch or tone used. Both pitch and pitch range, are found to be lower for non-tonal languages. A speech signal contains both speaker and language attributes. In tonal and non-tonal language classification, discriminating cues are extracted from the speech signal and fed to the different classifiers. This work is unique in the way that the speech signal is divided into its constituent syllables before doing any further processing for feature extraction, instead of considering the utterance as a whole. In this paper, the performance analysis of different classifiers is done at syllabic level for identifying Tonal and Non-Tonal languages. In this classification tasks artificial neural network (ANN) outperforms the other classifiers with the accuracy of 84.21%.
AB - Languages spoken by us, on the basis of how they use tone to convey a meaning, can be grouped into two categories: Tonal and Non-Tonal languages. Pitch is used as a figure of speech in the case of tonal languages. The connotation of a word changes depending on the pitch or tone used. Both pitch and pitch range, are found to be lower for non-tonal languages. A speech signal contains both speaker and language attributes. In tonal and non-tonal language classification, discriminating cues are extracted from the speech signal and fed to the different classifiers. This work is unique in the way that the speech signal is divided into its constituent syllables before doing any further processing for feature extraction, instead of considering the utterance as a whole. In this paper, the performance analysis of different classifiers is done at syllabic level for identifying Tonal and Non-Tonal languages. In this classification tasks artificial neural network (ANN) outperforms the other classifiers with the accuracy of 84.21%.
KW - Artificial neural network (ANN)
KW - K-Nearest Neighbor (k-NN)
KW - Support vector machine
KW - Syllable
KW - Tonal and non-tonal languages
KW - Vowel Onset Point (VOP)
UR - http://www.scopus.com/inward/record.url?scp=84997217032&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84997217032
T3 - Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016
SP - 1260
EP - 1264
BT - Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016
A2 - Hoda, M.N.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016
Y2 - 16 March 2016 through 18 March 2016
ER -