Low-rank constraint eigenphone speaker adaptation method for speech recognition

A low-rank constraint eigenphone speaker adaptation method is proposed. Original eigenphone speaker adaptation method performs well when the amount of adaptation data is sufficient. However, it suffers from server overfitting when insufficient amount of adaptation data is provided, possibly resulting in lower performance than that of the unadapted system. Firstly, a simplified estimation alogrithm of the eigenphone matrix is deduced in case of hidden Markov model-Gaussian mixture model (HMM-GMM) based speech recognition system with diagonal covariance matrices. Then, a low-rank constraint is applied to estimation of the eigenphone matrix. The nuclear norm is used as a convex approximation of the rank of a matrix. The weight of the norm is adjusted to control the complexity of the adaptation model. Finally, an accelerated proximal gradient method is adopted to solve the mathematic optimization. Experiments on an Mandarin Chinese continuous speech recognition task show that, the performance of the original eigenphone method is improved remarkably. The new method outperforms the maximum likelihood linear regression followed by maximum a posterriori (MLLR+MAP) methods under 5~50 s adaptation data testing conditions.