Result Analysis Of Different Wavelet Types Using  Speech Enhancement Algorithms

: The main aim of speech enhancement is to enhance the overall standard and intelligibility of the speech by decreasing the background noise level. This paper proposes a speech enhancement technique to enhance the speech signal in wavelet domain. The technique used is a combined approach of generalized spectral subtraction and MMSE log-STSA. The Experiment is conducted on male and female Database at different SNR levels.

REFERENCES

[1] Nikesh Bajaj and Rahul Kashyap “Extension of Wavelet Family in Fractional Fourier Domain” December 2012
[2] Hai-rong Jia, Xue-ying Zhan, Jing Bai, “A continuous differentiable wavelet threshold function for speech enhancement”,2013
[3] Paruav Goel and Anil Garg “Review of spectral subtraction Technique for speech enhancement” IJECT Vol 2 December 2011.
[4] Liuyang Gao and Yunfei “Speech enhancement algorithm based on improved spectral suntraction “ IEEE 2009.
[5] Anuradha R. Fukane, Shashikant L. Sahare, "Enhancement of Noisy Speech Signals for Hearing Aids", 2011
[6] Anuprita P. Pawar , Kirtimalini B. Choudhari and Madhuri A Joshi , “Review of Single Channel Speech Enhancement Methods in Spectral Domain” International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 7 No.11 ,2012.
[7] Ganga Prasad Surender, “A Review of Different Approaches of Spectral Subtraction Algorithms for Speech Enhancement”, Current Research in Engineering, Science and Technology (CREST) Journals,
[8] Paruav Goel and Anil Garg ”Develpoments in Spectral Subtraction for Speech Enhancement”IJERA Vol 2 February 2012.
[9] Tahsina Farah Sanam, Celia Shahnaz “An Approach for Noisy Speech Enhancement through soft thresholding function by Employing the Teager Energy Operation on Wavelet Packet” March 2012
[10] Yao Ren, Michael T. Johnson, and Jidong Tao “Perceptually motivated wavelet packet transform for bioacoustic signal enhancement”April 2008.
[11] Ruchismita Tripathy, Hrudaya Kumar Tripathy, “Unalike Methodologies of Feature Extraction & Feature Matching in Speech Recognition”, published in,2014.
[12] M.A.Anusuya, S.K.Katti, "Speech Recognition by Machine: A Review,"(IJCSIS) International Journal of Computer Science and Information Security, Vol. 6, No. 3, 2009, pp.181-205.
[13] 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.
[14] Sonia Sunny, David Peter S and K Poulose Jacob, “A Comparative Study of Parametric Coding and Wavelet Coding Based Feature Extraction Techniques in Recognizing Spoken Words”, CUBE 2012,Published in September 3–5, 2012, Pune, Maharashtra, India.
[15] Thiang, Suryo Wijoyo,,. “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), 2011.
[16] C. Demiroglu and T. Barnwell, “A Missing-Data Approach to Noise-Robust LPC Extraction for Voiced Speech Using Auxiliary Sensors”, Published in ICASSP 2005.
[17] L. S. Chee, Ooi Chia Ai, and S. Yaacob, "Overview of Automatic Stuttering Recognition System," in International Conference on Man- Machine Systems (ICoMMS 2009) Penang, Malaysia, 2009.
[18] Preeti Saini , Parneet Kaur, “Automatic Speech Recognition: A Review”, Published in International Journal of Engineering Trends and Technology- Volume4Issue2- 2013.