Linear Predictive Coding and Cepstral Analysis for Telugu Speech Recognition

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-47 Number-1
Year of Publication : 2017
Authors : P. Jeethendra, M. Chandrashekar
DOI :  10.14445/22312803/IJCTT-V47P106

MLA

P. Jeethendra, M. Chandrashekar "Linear Predictive Coding and Cepstral Analysis for Telugu Speech Recognition". International Journal of Computer Trends and Technology (IJCTT) V47(1):50-60, May 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This research work focused on feature extraction of in speech signal applied to Telugu Language processing. Telugu is a third largest spoken Indian Language which is widely spoken in Southern Indian States of India Talangana and Andhra Pradesh. Telugu is spoken in different accents even in the Telugu speaking geographic area. The feature extraction becomes more challenging when it is for a speaker independent speech recognition in nature. Every languages having different speaking styles called as accents or dialects. Identification of the accent before the speech recognition can improve performance of Speech recognition system. If the number of accents are more, then this becomes a crucial part of the study. If we can understand the different sources of variability in the signal accent then we can begin to approach the problem by separating them out in subsequent analysis stages Speech signal is analyzed in two ways – signal Processing and linguistic processing. During linguistic processing, signals are cut into chunks of varying degrees of abstraction such as acoustic-phonetic segments(APS), allophones, phonemes, morphophonemic, etc, which will be ultimately correlated with the letters in the script of a language by computational technique. Among the various techniques presently available in speech processing technology such as Fast Fourier Transforms, Linear Predictive Coding, Mel Frequency Cepstral Coefficients, Cepstral Analysis, Discrete Wavelet Transforms, Wavelet Packet Transforms, Hybrid Algorithm DWPD and their applications in speech processing, we have studied, Out of these LPC and Cepstral Analysis in this research work.

References
1. Steve Cassidy - Speech Recognition Department of Computing, Macquarie University, Sydney,Australia , This email address is being protected from spambots. You need JavaScript enabled to view it. (2002)
2. L. R. Rabiner and R. W. Schafer. Digital Processing of Speech Signals. Prentice Hall, Englewood Cliffs, New Jersey, 1978. 3. Douglas O’Shaugnessy. Speech Communication Human and Machine. Addison Wesley Books, 1978.
4. M. M. Sondhi. New Methods of Pitch Extraction. IEEE Trans. Audio and Electroacoustics, Vol. AU-16, No. 2, pp. 262-266, June 1968.
5. Harshita Gupa, Divya Gupta Department of Computer science and engineering, Amity University Uttar Pradesh, Noida, India, LPC and LPCC method of feature extraction in Speech Recognition System - Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference, 14-15 Jan. 2016, IEEE Xplore: 11 July 2016
6. PERI BHASKARARAO, Salient phonetic features of Indian languages in speech technology, Tokyo University of Foreign Studies, Tokyo, Japan, published in Sa¯dhana¯ Vol. 36, Part 5, October 2011, pp. 587–599._c Indian Academy of Sciences.
7. Manish P. Kesarkar, FEATURE EXTRACTION FOR SPEECH RECOGNITON, M.Tech. Credit Seminar Report, Electronic Systems Group, EE. Dept, IIT Bombay, Submitted November2003
8. H. Hermansky, B. A. Hanson, and H. Wakita, "Perceptually based processing in automatic speech recognition," Proc. IEEE Int. Conf. on Acoustic, speech, and Signal Processing," pp. 1971- 1974, Apr.1986.
9. Omniglot online encyclopedia Telugu%20alphabet,% 20 pronunciation% 20and%20 language.html 10. Thomas F. Quatieri , Discrete-Time Speech Signal Processing, , Chapter 7
11. Indexie .html
12. Tomyslav Sledevic,? Artu¯ras Serackis, Gintautas Tamulevici? us, Dalius Navakauskas, International Journal of Electrical,Computer, Electronics and Communication on Evaluation of Features Extraction Algorithms for a Real- Time Isolated Word Recognition System Vol:7 No:12, 2013
13. Shanthi Therese Chelpa Lingam, International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) a Review of Feature Extraction Techniques in Automatic Speech Recognition, Volume No.2, Issue No.6, pp : 479- 484 1 June 2013
14. Santosh K.Gaikwad and Pravin Yannawar, A Review, International Journal of Computer Applications A Review on Speech Recognition Technique Volume 10– No.3, November 2010
15. Navnath S Nehel and Raghunath S Holambe Journal on Audio, Speech, and Music Processing, on DWT and LPC based feature extraction methods for isolated word recognition, 2012
16. Rybach, D.; C. Gollan; G. Heigold; B. Hoffmeister; J. Lööf; R. Schlüter; H. Ney (September 2009). "The RWTH Aachen University Open Source Speech Recognition System". Interspeech-2009: 2111–2114.
17. Shreya Narang et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.3, March- 2015, pg. 107-114
18. A. Shiva Prasad et al Speech Features Extraction Techniques for Robust Emotional Speech Analysis/Recognition Indian Journal of Science and Technology, Vol 10(3), DOI: 10.17485/ijst/2017/v10i3/110571, January 2017

Keywords
Feature extraction, Speaker Independent, Linear Predictive Coding (LPC), Cepstral Analysis, Telugu, Acoustic Phonetic Segments (APS).