Sonia Sunny
ABSTRACT
Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. In this paper, a comparative study of different feature extraction techniques like Linear Predictive Coding (LPC), Discrete Wavelet Transforms (DWT) and Wavelet packet Decomposition (WPD) are employed for recognizing speaker independent spoken isolated words. Voice signals are sampled directly from the microphone and then they are processed using these three techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). This work includes three speech recognition methods. First one is a hybrid approach with LPC and ANN, second method uses a combination of DWT and ANN and the third one utilizes a combination of WPD and ANN. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. All the three methods produce good recognition accuracy. LPC based method produced an accuracy of 81.20%, DWT gave an accuracy of 90% and WPD produced a recognition accuracy of 87.50%. Thus wavelet based methods are found to be more suitable for recognizing speech because of their multi-resolution characteristics and efficient time frequency localizations. Moreover, wavelet methods have a better capability to model the unvoiced sound details.
- Lawrence R., 1997. Applications of Speech Recognition in the Area of Telecommunications, Proc. of IEEE Workshop on Automatic Speech Recognition and Understanding, (Dec. 17, 1997), 501--510.Google Scholar
- recognition.http://www.learnartificialneuralnetworks.com/speechrecognition.htmlGoogle Scholar
- Kuldeep Kumar, R. K. Aggarwal, 2011. Hindi Speech Recognition System Using Htk, International Journal of Computing and Business Research, Vol. 2, Issue 2.Google Scholar
- Sarikaya R., Gao Y., Saon G., 2004. Fractional Fourier transform features for speech recognition, Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, (17-21 May 2004 ), Vol.1, 529--532.Google Scholar
- Polur PD, Miller GE, 2005. Experiments with fast Fourier transform, linear predictive and cepstral coefficients in dysarthric speech recognition algorithms using hidden Markov Model, IEEE Trans Neural Syst. Rehabil Eng., 13 (4), 558--61.Google ScholarCross Ref
- Febe de Wet, Bert Cranen, Johan De Veth, Loe Boves, 2001. A comparison of LPC and FFT-based acoustic features for noise robust ASR, Proc. of 7th European Conference on Speech Communication and Technology, (Aalborg, Denmark, September 3-7, 2001).Google Scholar
- Anuj Mohamed, K. N. Ramachandran Nair, 2010. Continuous Malayalam speech recognition using Hidden Markov Models, Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, No. 49. Google ScholarDigital Library
- Cini Kurian, Kannan Balakrishnan, 2011. Automated Transcription System for Malayalam Language, International Journal of Computer Applications, Vol 19, No.5, 5--10.Google ScholarCross Ref
- Cini Kurian, Firoz Shah.A, Kannan Balakrishnan, 2010. Isolated Malayalam Digit Recognition Using Support Vector Machines, Proc. of. IEEE International Conference on Communication Control and Computing Technologies, (Oct. 7-9, 2010), 692--695Google Scholar
- Bharti W. Gawali, Santosh Gaikwad, Pravin Yannawar, Suresh C. Mehrotra, 2010. Marathi Isolated Word Recognition System using MFCC and DTW Features, Proc. of Int. Conf. on Advances in Computer Science, (Dec 21-22), 143--146.Google Scholar
- Santhosh V. Chapaneri, 2012. Spoken Digits Recognition using Weighted MFCC and Improved Features for Dynamic Time Warping, International Journal of Computer Applications, Vol 40, No.3, 6--12.Google ScholarCross Ref
- Paul A. K., Das D., Kamal M. M., 2009. Bangla Speech Recognition System Using LPC and ANN, Seventh International Conference on Advances in Pattern Recognition, IEEE Xplore, (Kolkata, Feb. 4-6 2009), 171--174. Google ScholarDigital Library
- Thiang, Suryo Wijoyo, 2011. Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot, Proc. of. Int. Conf. on Information and Electronics Engineering, IPCSIT vol.6, (IACSIT Press, Singapore).Google Scholar
- Ooi Chia Ai, M. Hariharan, Sazali Yaacob, Lim Sin Chee, 2012. Classification of speech dysfluencies with MFCC and LPCC features, Expert Systems with Applications, Vol.39 (2), 2157--2165. Google ScholarDigital Library
- Engin Avci, Zuhtu Hakan Akpolat, 2006. Speech recognition using a wavelet packet adaptive network based fuzzy inference system, Expert Systems with Applications, Volume 31, Issue 3, pp. 495--503.Google ScholarCross Ref
- Vimal Krishnan V. R, Babu Anto P, 2009. Features of Wavelet Packet Decomposition and Discrete Wavelet Transform for Malayalam Speech Recognition, International Journal of Recent Trends in Engineering, Vol. 1(2), 93--96.Google Scholar
- Yang Jie, 2009. Noise robust speech recognition by combining speech enhancement in the wavelet domain and Lin-log RASTA, ISECS International Colloquium on Computing, Communication, Control, and Management, IEEE Xplore, (Aug. 8-9, 2009), Vol. 2, 415--418.Google Scholar
- Shivesh Ranjan, 2010. Exploring the Discrete Wavelet Transform as a Tool for Hindi Speech Recognition, International Journal of Computer Theory and Engineering, Vol. 2, No. 4, 642--646.Google ScholarCross Ref
- Sonia Sunny, David Peter S., K. Poulose Jacob. 2011, Wavelet Packet Decomposition and Artificial Neural Networks based Recognition of Spoken Digits, International journal of machine intelligence, Vol.3, issue 4, 318--321.Google Scholar
- M.A.Anusuya, 2011. Comparison of Different Speech Feature Extraction Techniques with and without Wavelet Transform to Kannada Speech Recognition, International Journal of Computer Applications, Vol. 26, No.4, 19--24.Google ScholarCross Ref
- Picone J. W., 1993. Signal Modelling Technique in Speech Recognition, Proc. of the IEEE, Vol. 81, No.9, 1215--1247.Google ScholarCross Ref
- Rabiner L., Juang B. H., 1993. Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, NJ. Google ScholarDigital Library
- Jeremy Bradbury, 2000. Linear Predictive Coding.Google Scholar
- S. Mallat, 1999. A wavelet Tour of Signal Processing, Academic Press, San Diego.Google Scholar
- K. P Soman, K. I Ramachandran, N. G Resmi, 2010. Insight into Wavelets From Theory to Practice, PHI Learning Private Ltd, New Delhi.Google Scholar
- Elif Derya Ubeyil, 2009. Combined Neural Network Model Employing Wavelet Coefficients for ECG Signals Classification, Digital signal Processing, Vol 19, 297--308. Google ScholarDigital Library
- S. Chan Woo, C. Peng Lin, R. Osman, 2001. Development of a Speaker Recognition System using Wavelets and Artificial Neural networks, Proc. of 2001 Int. Symposium on Intelligent Multimedia, Video and Speech processing, (Hong Kong, May 2-4, 2001), 413--416.Google Scholar
- S. Kadambe, P. Srinivasan, 1994. Application of Adaptive Wavelets for Speech, Optical Engineering, Vol 33(7), 2204--2211.Google ScholarCross Ref
- S. G. Mallat 1989. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol.11, 674--693. Google ScholarDigital Library
- http://en.wikipedia.org/wiki/Discrete_wavelet_transformGoogle Scholar
- Fecit Science and Technology Production Research Center, 2003. Wavelet Analysis and Application by MATLAB6.5 {M}, Electronics Industrial Press, Beijing.Google Scholar
- http://en.wikipedia.org/wiki/Wavelet_packet_decompositionGoogle Scholar
- Y. Hao, X. Zhu, 2000. A New Feature in Speech Recognition based on Wavelet Transform, Proc. of. IEEE 5th Inter. Conf. on Signal Processing, (Aug. 21-25, 2000), Vol 3, 1526--1529.Google Scholar
- Freeman J. A, Skapura D. M., 2006. Neural Networks Algorithm, Application and Programming Techniques, Pearson Education. Google ScholarDigital Library
- Economou K., Lymberopoulos D., 1999. A New Perspective in Learning Pattern Generation for Teaching Neural Networks, Volume 12, Issue 4-5, 767--775. Google ScholarDigital Library
- Eiji Mizutani, James W. Demmel, 2003. On Structure-exploiting Trust Region Regularized Nonlinear Least Squares Algorithms for Neural-Network Learning, Neural Networks, Volume 16, 745--753. Google ScholarDigital Library
- Anil K. Jain, Robert P. W. Duin, Jianchang Mao, 2000. Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, 4--37. Google ScholarDigital Library
- Sonia Sunny, David Peter S, K Poulose Jacob, 2012. Optimal Daubechies Wavelets for Recognizing Isolated Spoken Words with Artificial Neural Networks Classifier, International Journal of Wisdom Based Computing, Vol. 2(1), 35--41.Google Scholar
Index Terms
- A comparative study of parametric coding and wavelet coding based feature extraction techniques in recognizing spoken words
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