|
| A Meter Classification System for Spoken Persian Poetries
|
|
Full
text: |
 PDF(141.5KB) |
|
|
Source |
Signal Processing: An International Journal (SPIJ) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(1.28MB) |
|
Volume: 5 Issue: 4 |
| |
Pages: NULL |
|
Publication
Date: September / October 2011 |
|
ISSN
(Online): 1985-2339 |
|
|
|
|
|
Pages |
174 - 184 |
|
Author(s) |
|
|
|
Published
Date |
05-10-2011 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Automatic Meter Detection, Syllable Classification, Utterance Syllabification, Support Vector Machines, Dynamic Time Warping, Poetries Catagorization |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. Google Scholar |
| 4. Google Scholar |
| 5. Docstoc |
| |
|
| |
|
|
| In this article, a meter classification system has been proposed for Persian poems based on features that are extracted from uttered poem. In the first stage, the utterance has been segmented into syllables using three features, pitch frequency and modified energy of each frame of the utterance and its temporal variations. In the second stage, each syllable is classified into long syllable and short syllable classes which is a historically convenient categorization in Persian literature. In this stage, the classifier is an SVM classifier with radial basis function kernel. The employed features are the syllable temporal duration, zero crossing rate and PARCOR coefficients of each syllable. The sequence of extracted syllables classes is then softly compared with classic Persian meter styles using dynamic time warping, to make the system robust against syllables insertion, deletion or classification and poems authorities. The system has been evaluated on 136 poetries utterances from 12 Persian meter styles gathered from 8 speakers, using k-fold evaluation strategy. The results show 91% accuracy in three top meter style choices of the system. |
| |
|
| |
|
| |
| 1 |
Z. S. He, W.T. Liang, L. Y. Li, Y.F. Tian, "SVM-Based Classification Method For Poetry Style", Proceedings of Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, Aug 2007, PP. 2936-2940.
|
|
|
| 2 |
X.Wang, Y. Liu, L.Cai, "Entering Tone Recognition in a Support Vector Machine Approach", Proceedings of Fourth International Conference on Natural Computation, Aug. 2007, PP. 61-65.
|
|
|
| 3 |
Y.YI, Z. S. He, L. Y. Li, T. Yi, E. Yi, "Advanced Studies On Traditional Chinese Poetry Style Identification", Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhu, Aug. 2005, PP. 3830-3833.
|
|
|
| 4 |
N. Sataravaha. "Tone classification of syllable segmentation Thai speech based on multilayer perceptron" Acoustical Society of America Journal, Volume 111, Issue 5, 2002,pp. 2366-2366.
|
|
|
| 5 |
H.R. Tizhoosh, R.A. Dara, "On Poem Recognition", Pattern Analysis and Application, Vol. 9, No.4, Nov. 2006, PP. 325-338.
|
|
|
| 6 |
H. Meinedo "The use of syllable segmentation information in continuous speech recognition hybrid systems " Applied to the Portuguese language , IEEE Transaction on Acoustic , speech and signal processing. 0-1623-8, 2000
|
|
|
| 7 |
Vahidian-Kamkar T, "Persian Poetries Meter and Rhyme", Nashr-e-Daneshgahi, 1990. (Published in Persian)
|
|
|
| 8 |
Shamisa S, "An Introduction to Meter and Rhyme", Ferdos Publications, 1992, (Published in Persian)
|
|
|
| 9 |
Liany-Yan Li , " Poetry stylistic analysis technique based on term connection " . IEEE Speech and signal processing. 0-7803-8403, 2004
|
|
|
| 10 |
Hartmut R.Pfitzinger " syllable detection in read and spontaneous speech " 13-4822-13324, 1996
|
|
|
| 11 |
I. Kopecek: "Speech recognition and syllable segmentation"; in Proceedings of the Workshop on Text, Speech and Dialogue - TSD'99, Lectures Notes in Artificial Intelligence 1692, Springer-Verlag, 1999, pp. 203-208.
|
|
|
| 12 |
Dubnowski, J.J. & Rabiner "Real time digital hardware Pitch detector". IEEE Transactions on Acoustics speech and signal processing. 24(1), 2-8. 1976
|
|
|
| 13 |
V. Vapnik. The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995.
|
|
|
| 14 |
C. J. C. Burges. "A Tutorial on Support Vector Machines for Pattern Recognition" Knowledge Discovery and Data Mining, 2(2), 1998.
|
|
|
| 15 |
P.H.Chen, C.J. Lin, B.S.ch¨olkopf, "A Tutorial on v-Support Vector Machines" Applied Stochastic Models in Business and Industry 21(2), pages 111-136, 2005.
|
|
|
| 16 |
L. Rabiner and B.H. Juang. Fundamentals of Speech Recognition. Prentice Hall, 1993.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Saeid Hamidi : Colleagues
|
|
| Farbod Razzazi : Colleagues
|
|
