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Reseach Article

Accent Recognition for Indian English using Acoustic Feature Approach

by Santosh Gaikwad, Bharti Gawali, K. V. Kale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 7
Year of Publication: 2013
Authors: Santosh Gaikwad, Bharti Gawali, K. V. Kale
10.5120/10479-5213

Santosh Gaikwad, Bharti Gawali, K. V. Kale . Accent Recognition for Indian English using Acoustic Feature Approach. International Journal of Computer Applications. 63, 7 ( February 2013), 25-32. DOI=10.5120/10479-5213

@article{ 10.5120/10479-5213,
author = { Santosh Gaikwad, Bharti Gawali, K. V. Kale },
title = { Accent Recognition for Indian English using Acoustic Feature Approach },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 7 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number7/10479-5213/ },
doi = { 10.5120/10479-5213 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:13:32.985646+05:30
%A Santosh Gaikwad
%A Bharti Gawali
%A K. V. Kale
%T Accent Recognition for Indian English using Acoustic Feature Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 7
%P 25-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accent is the basic pattern of acoustic feature and pronunciation. It can identify the person's social and linguistic background. It is an important source of inter as well as intra speaker variability. The accent dependent dictionary or model can be used to improve accuracy of speech recognition system. In this study we present an experimental approach of acoustic speech feature for Marathi & Arabic accents for English speaking. The detail study of acoustics correlates the accent using formant frequency, energy and pitch characteristics. The database consists of speech from speaker with Marathi as their mother tongue and speakers from Iraq with Arabic language as mother tongue. Both the speakers were asked to speak English number from zero to nine. Through experimental results the fifth formant frequency found to be very effective for accent recognition.

References
  1. A. Ikeno and J. H. L. Hansen, "The effect of listener accent background on accent perception and comprehension", EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2007, Article ID 76030, 8 pages, 2007.
  2. C. Pedersen and J. Diederich, "Accent classification using support vector machines", 6th IEEE/ACIS International Conference on Computer and Information Science , ICIS 2007.
  3. Asher and G. Garcia (19691, "The optimal age to learn a foreign language", Modern Language J. , Vol. 38, pp. 334- 341.
  4. L. R. Rabiner and J. G Wilpon (1977),"Speaker Independent isolated word recognition for a moderate size (54 word) vocublary", IEEE Trans. Acoust. Speech signal processing Vol. 27,pp. 538-587.
  5. V. Gupta and P. Mermelstein (1982). "Effects of speaker accent on the performance of a speaker-independent, isolated-word recognizer", J. Acoust. Sot. Amer. , Vol. 71, pp. 1581- 1587.
  6. W. J. Barry, C. E. Hoequist and F. J. Nolan (19891, "An approach to the problem of regional accent in automatic speech recognition", Computer Speech and Language, Vol. 3, pp. 355-366.
  7. A. Ljolje and F. Fallside (1987), "Recognition of isolated prosodic patterns using hidden Markov models", Computer Speech and Language, Vol. 2, pp. 27-33.
  8. J. C. Wells, Accents of English, volume:1,2, Cambridge University Press, 1982.
  9. C. Pedersen and J. Diederich, "accent classification using support vector machines", 6th IEEE/ACIS International Conference on Computer and Information Science , ICIS 2007.
  10. L. M. Arslan and J. H. L. Hansen, "Language accent classification in American English", Speech Communication, Revised January 29, 1996.
  11. Zheng, D. C. , Dyke, D. , Berryman, F. , & Morgan, C. (2011). A new approach to acoustic analysis of two British regional accents—Birmingham and Liverpool accents. International Journal of Speech Technology, 15(2), 77–85. doi:10. 1007/s10772-011-9123-3.
  12. Rabiee, A. , & Setayeshi, S. (2010). Persian Accents Identification Using an Adaptive Neural Network. 2010 Second International Workshop on Education Technology and Computer Science, 7–10. doi:10. 1109/ETCS. 2010. 273
  13. Hansen, J. H. L. , Arslan, L. M. , & Carolina, N. (1997). Frequency characteristics of foreign accented speech, Duke University Department of Electrical Engineering, 1123–1126.
  14. Tang, H. , & Ghorbani, A. A. (n. d. ). Accent Classification Using Support Vector Machine and Hidden Markov Model, (1), 3–4.
  15. Arslan, L. M. , & Hansen, J. H. L. (1996). Language accent classification in American English. Speech Communication, 18(4), 353–367. doi:10. 1016/0167-6393(96)00024-6 .
  16. Hansen, J. H. L. , Arslan, L. M. , & Carolina, N. (1997). Frequency characteristics for foreign accented speech, Duke University Department of Electrical Engineering, 1123–1126.
  17. Kat, L. I. U. W. , Fung, P. , Bay, C. W. , & Kong, H. (1999). Fats accent identification and accented speech recognition, 3–6.
  18. Xuejing Sun . Pitch accent prediction using ensemble machine learning, Department of Communication Sciences and Disorders, Northwestern University 2299 N. Campus Dr. , Evanston, IL 60208, USA.
  19. Variation of vocal format and speech [online] http://hyperphysics. phy-astr. gsu. edu/hbase/music/vowel2. html viewed on 02 Sept 2012.
  20. Zissman, M. (1993). Automatic language identification using Gaussian mixture and hidden Markov models. Acoustics, Speech, and Signal Processing, 1993 …, 399–402. Retrieved from http://ieeexplore. ieee. org/xpls/abs_all. jsp?arnumber=319323
  21. Hanani, A. , Russell, M. , & Carey, M. J. (2011). Speech-based identification of social groups in a single accent of British English by humans and computers. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4876–4879. doi:10. 1109/ICASSP. 2011. 5947448
  22. Zheng, Y. , Sproat, R. , Gu, L. , Shafran, I. , Zhou, H. , Su, Y. , Jurafsky, D. , et al. (n. d. ). Accent Detection and Speech Recognition for Shanghai-Accented Mandarin, 7–10.
  23. John N. Holmes, Wendy J. Holmes and Philip N. Garner "Using formant frequencies in speech recognition, Speech Technology Consultant, 19 Maylands Drive, Uxbridge, UB8 1BH, U. K.
  24. P. Schmid and E. Barnard, "Robust, N-Best Formant Tracking", Proc. EUROSPEECH'95, pp. 737-740, Madrid, 1995
  25. L. Welling and H. Ney, "A Model for Efficient Formant Estimation", Proc. IEEE ICASSP, pp. 797-800, Atlanta, 1996
  26. Y. Laprie and M. -O. Berger, "Active Models for Regularizing Formant Trajectories", Proc. ICSLP, pp. 815-818, Banff, 1992
  27. Levow, G. (2009). Investigating Pitch Accent Recognition in Non-native Speech, (August), 269–272.
  28. Ishi, C. T. , Hirose, K. , & Minematsu, N. (2003). Mora F0 representation for accent type identification in continuous speech and considerations on its relation with perceived pitch values. Speech Communication, 41(2-3), 441–453. doi:10. 1016/S0167-6393(03)0014-1
  29. Stantic, D. , & Jo, J. (2012). Accent Identification by Clustering and Scoring Formants, 232–237.
Index Terms

Computer Science
Information Sciences

Keywords

Accent Acoustic Energy Formant Frequency Pitch Foreign