Hybrid Filter for Enhancing Input Microphone-Based Discriminative Model

Abstract

Voice denoising is the process of removing undesirable voices from the voice signal. Within the environmental noise and after the application of speech recognition system, the discriminative model finds it difficult to recognize the waveform of the voice signal. This is due to the fact that the environmental noise needs to use a suitable filter that does not affect the shaped waveform of the input microphone. This paper plans to build up a procedure for a discriminative model, using infinite impulse response filter (Butterworth filter) and local polynomial approximation (Savitzky-Golay) smoothing filter that is a polynomial regression on the signal values. Signal to noise ratio (SNR) was calculated after filtering to compare the results after and before adding the Savitzky-Golay smoothing filter. This procedure showed better results for the filtering of ambient noise and protecting a waveform from distortion, which makes the discriminative model more accurate when recognizing voice. Our procedure for preprocessing was developed and successfully implemented on a discriminative model by using MATLAB.