Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing

Abstract

Speech data collected under uncontrolled environment need to be processed to build a robust automatic speech recognition system. In this paper, a method is proposed to process the degraded speech signal. Initially, the significance of the spectral subtraction with voice activity detection (SS-VAD) and magnitude squared spectrum estimators are studied for different types of noises. In SS-VAD method, the degraded speech data is sampled and windowed into 50% overlapping. The VAD is used to detect the voiced regions of speech signal. The minimum mean square error-short time power spectrum, minimum mean square error-spectrum power based on zero crossing (MMSE-SPZC) and maximum a posteriori estimators are studied individually. These MSS estimators are implemented on the assumption that the magnitude squared spectrum of the degraded speech signal is the sum of the clean (original) speech signal and noise model. The experimental results show that the MMSE-SPZC estimator gives better performance compared to the other two methods. This estimator is combined with SS-VAD method to improve the performance. In this paper, the combined SS-VAD and MMSE-SPZC method, yields better speech quality by reducing noise in degraded speech signal compared to the individual methods.