Data-Driven Temporal Filtering on Teager Energy Time Trajectory for Robust Speech Recognition

Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation (TEO) based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include: principle component analysis (PCA) based filters, linear discriminant analysis (LDA) based filters and minimum classification error (MCE) based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.