Isolated Word Recognition Based on Combination of Multiple Noise-Robust Techniques

Abstract

Although many noise-robust techniques have been presented, the improvement under low SNR condition is still insufficient. The purpose of this paper is to achieve the high recognition accuracy under low SNR condition with low calculation costs. Therefore, this paper proposes a novel noise-robust speech recognition system that makes full use of spectral subtraction (SS), mean variance normalization (MVN), temporal filtering (TF), and multi-condition HMMs (MC-HMMs). First, from the results of SS with clean HMMs, we obtained the improvement from 46.61% to 65.71% under 0 dB SNR condition. Then, SS+ MVN+TF with clean HMMs improved the recognition accuracy from 65.71% to 80.97% under the same SNR condition. Finally, we achieved the further improvement from 80.97% to 92.23% by employing SS+MVN+TF with MC-HMMs.