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http://dx.doi.org/10.3745/KIPSTB.2002.9B.2.215

Phonetic Acoustic Knowledge and Divide And Conquer Based Segmentation Algorithm  

Koo, Chan-Mo (Dept.of Industry Engineering, Graduate School of Ajou University)
Wang, Gi-Nam (Dept.of Industry Engineering, Ajou University)
Publication Information
The KIPS Transactions:PartB / v.9B, no.2, 2002 , pp. 215-222 More about this Journal
Abstract
This paper presents a reliable fully automatic labeling system which fits well with languages having well-developed syllables such as in Korean. The ASL System utilize DAC (Divide and Conquer), a control mechanism, based segmentation algorithm to use phonetic and acoustic information with greater efficiency. The segmentation algorithm is to devide speech signals into speechlets which is localized speech signal pieces and to segment each speechlet for speech boundaries. While HMM method has uniform and definite efficiencies, the suggested method gives framework to steadily develope and improve specified acoustic knowledges as a component. Without using statistical method such as HMM, this new method use only phonetic-acoustic information. Therefore, this method has high speed performance, is consistent extending the specific acoustic knowledge component, and can be applied in efficient way. we show experiment result to verify suggested method at the end.
Keywords
Automatic segmentation and labelling; Signal Localization; Case Study; Divide-And-Conquer;
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  • Reference
1 엄태수, '한국어의 음운규칙 연구', pp.172-264, 1999
2 F. Brugnara, D. Falavigua, and M. Omologo, 'Automatic segmentation and labelling of speech based on hidden Markov models,' Speech Communication pp.357-370, 1993   DOI   ScienceOn
3 J. P. Hosom, 'Automatic time alignment of phonemes using acoustic-phonetic information,' Ph.d. thesis, Computer Science and Engineering, Oregon Graduate Institute of Science and Technology, May, 2000
4 E. Vidal and A. Marzal, 'A review and new approaches for automatic segmentation of speech signals, Signal Processing V : Theories and Applications (L. Torres, E. Masgrau, and M. A. Lagunas, eds.),' Elsevier Science Publisher, Amsterdam, pp.43-53, 1990
5 K. Kvale, 'On the connection between mannual segmentation conventions and error made by automatic segmentation,' Proceedings of ICSLP'94 (Yokohama, Japan), pp.1667-1670, September, 1994
6 'http://htk.eng.cam.ac.uk,' Hidden Markov Model Tool Kit Homepage
7 허웅, '국어음운학', 정음사, pp.129-280, 1984
8 J. R. Class, 'Finding acoustic regularities in speech : Application to phonetic recognition,' Ph.D. Thesis, MIT Press, May, 1988
9 R. Andre-Obrecht, 'A new statistical approach for the automatic segmentation of continuous speech signals,' IEEE Trans. ASSP 36, pp.29-40, 1988   DOI   ScienceOn
10 S. Cox, R. Brady, and P. Jackson, 'Techniques for accurate automatic annotation of speech wave forms,' Proceedings of ICSLP'98 (Sydney, Australia), pp.1947-1950, December, 1998
11 P. Cosi, 'SLAM : A PC-based multi-level segmentation tool, Speech Recognition and Coding : New Advances and Trends (A. J. R. Ayuso and J. M. L. Soler, eds.),' Computer and System Sciences, Vol.147, Springer-Verlag, New York, pp.124-127, 1995