Exploiting Signal Linear Trend in the Time Domain to Enhance Speech Feature

Speech feature extraction usually begins with transforming the signal from the time domain to the frequency domain via integral transforms. Although human speech contains sine-shape waves and linear trends in the time domain, the linear trend is usually ignored. This research proposes two new methods to strengthen the speech feature vector using these unused elements. Before transforming the speech frames into the frequency domain, we use linear regression to identify each speech frame’s linear trend or linear envelope in the time domain. Then we remove the impact of that trend to normalize the signal and emphasize the stationary elements in the signal. The proposed feature vector includes parameters from the linear envelope and the conventional vectors as a spectrum or MFCC. Our new features not only emphasize the stationary property in the speech signal, but also improve the result for speech recognition in terms of error rate. Experimental results demonstrate that the impact of the linear envelope is significant and the linear envelope subtraction is a meaningful stage.