SPEECH ENHANCEMENT SYSTEM USING LABVIEW

Speech enhancement has become one of the most important tools of the modern generation and is widely used in various fields for various purposes. The past decade has seen dramatic progress in speech recognition technology, to the extent that systems and high-performance algorithms have become accessible. Speech enhancement depends on signal processing. Speech enhancement techniques are widely used to enhance the quality and intelligibility of the speech signal in the noisy environment. Conventional noise reduction methods introduce more residual noise and speech distortion. The existing algorithms fail when there are abrupt changes in the noise level. To overcome the shortcomings of the conventional methods, improved noise tracking algorithm is proposed in this paper for speech enhancement. The noise signal is estimated for the existing and the proposed methods. Results are simulated using LabView. This report shows how to recognize and enhance the speech using filters in lab view. Predictable noise reduction methods initiate more enduring noise and speech alteration. The existing algorithms not succeed when there are sudden changes in the noise level. To overcome the shortcomings of the unadventurous methods, enhanced noise tracking algorithm is future in this paper for speech enhancement. The noise signal is estimated for the accessible and the future methods. Calculate the SNR (signal to noise ratio) value of input signal, input signal plus added noise and filtered signal in order to measure the improved SNR value. By using filters we will get the enhanced speech signal with reduced noise. The aim speaker, and the signal-to noise ratio (SNR) specifically to switch definite speakers, noise types and SNRs, are competent of achieving hefty improvement in estimated speech quality (SQ) and speech clearness. A noisy sound of an untrained speech is processed finally; we   compare the proposed algorithm with different speech enhancement algorithms.  The contribution of all components of the proposed algorithm was analyzed signifying their collective importance.