This paper is published in Volume-3, Issue-4, 2017
Area
Speech Signal Processing
Author
Dr. D. Deepa
Co-authors
Dr. C. Poongodi
Org/Univ
Bannari Amman Institute of Technology, Coimbatore, Tamil Nadu, India
Pub. Date
23 August, 2017
Paper ID
V3I4-1332
Publisher
Keywords
Speech Enhancement, Adaptive Filtering, Normalized Least Mean Square Algorithm, Variable Step Size, Speech Recognition, Signal to Noise Ratio, Mean Square Error, IS Distance

Citationsacebook

IEEE
Dr. D. Deepa, Dr. C. Poongodi. Speech Enhancement Using Dual Transform-Normalized LMS Algorithm for Speech Recognition Application, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Dr. D. Deepa, Dr. C. Poongodi (2017). Speech Enhancement Using Dual Transform-Normalized LMS Algorithm for Speech Recognition Application. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.

MLA
Dr. D. Deepa, Dr. C. Poongodi. "Speech Enhancement Using Dual Transform-Normalized LMS Algorithm for Speech Recognition Application." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.

Abstract

Speech enhancement is one of the important preprocessing techniques required for any speech processing applications. This work represents dual channel speech enhancement where the desired signal is available and the input noisy speech is enhanced based on the reference signal, this method of speech enhancement can be used in robots where the machine can recognize the comments given by human. In this paper noisy and the desired speech signals are dual transformed using discrete cosine transform and Hadamard transform and applied to the adaptive filter using Normalized Least Mean Square algorithm. In NLMS the step size parameter is varying based on the input signal rather in LMS the step size is constant. This variable step size will lead to fast convergence of noisy speech towards desired speech and the enhanced signal gives better performance compared to conventional LMS algorithm. The performance analysis is done through various subjective and objective measures.
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