A soft computing approach to improve the robustness of on-line ASR in previously unseen highly non-stationary acoustic environments

This paper presents a soft noise compensation algorithm in the feature space to improve the noise robustness of HMM-based on-line automatic speech recognition (ASR) in unknown highly non-stationary acoustic environments. Current hard computing techniques fail to track and compensate the non-stationary noises properly in previously unseen acoustic environments. The proposed soft noise compensation algorithm is based on a joint additive background noises and channel distortions compensation (JAC) technique in feature space. In this novel soft JAC (SJAC), we use an evolutionary dynamic multi-swarm particle swarm optimization (DMS-PSO)-based soft computing (SC) technique in the front-end, and a frame synchronous bias compensation technique in the back-end of the ASR, respectively, for frame adaptive modeling and compensation of the background additive noises and channel distortions in feature space that are highly non-linear and non-Gaussian. From the experimental results, we find that the proposed evolutionary DMS-PSO-based SJAC technique achieves significant improvement in recognition performance of on-line ASR compared to our previously developed baseline Bayesian on-line spectral change point detection (BOSCPD)-based SJAC technique when evaluated over the Aurora 2 speech database.