VTS feature compensation based on two-layer GMM structure for robust speech recognition
Lin Zhou, Haijing Li, Ying Chen, Zhenyang Wu, Yong Jun Lu · 2016
In this paper, a two-layer Gaussian Mixed Model (GMM) structure for Vector Taylor Series (VTS) feature compensation is proposed for robust speech recognition. Since GMM with the numerous mixture components is used for VTS, the computation complexity of VTS is extremely huge. To deal with this issue, we propose two-layer GMM structure for VTS. In detail, the GMM with fewer mixture components is utilized to estimate the mean and variance of noise. With the estimated noise parameters, the second GMM with more mixtures is employed to map noisy features to clean features. The simulation results show that the proposed algorithm significantly reduces the computation complexity of VTS. Meanwhile, its performance is well performed as that of the traditional system.