A quantitative real time data analysis in vehicular speech environment with varying SNR

Abstract

The purpose of this paper is to compare the performance of two common filters operating on noisy speech recorded in automobiles travelling at various speeds. The filters are based on Spectral Subtraction (SS) and Kalman Filtering (KF). The literature contains studies based on simulated data whereas this paper uses real time data collected in car's in search of an optimal solution. The comparisons were based on real recorded samples containing noisy speech signals with durations of approximately 2 minutes each. Different cases of noise levels which represent the most common situations experienced by drivers were created. The different settings used include varying car speeds (e.g., 40 mph, 70 mph), varying fan power, and window positions settings. The study was carried out using three different car models. The measured noisy voice signals were filtered using the different filtering techniques and the resulting filtered signals were compared in the time domain and the frequency domain, both quantitatively and psychometrically. Furthermore, the quantitative analysis approach was applied to the results for more accurate interpretation. Results show that SS outperforms KF in noise reduction, and with much less speech distortion at the different Signal to Noise Ratios (SNRs) tested. The audio test results subjected to human listening are comparable with the simulation results. Overall, SS showed superior performance over KF in vehicular hands-free speech applications.