A Closed-Form Solution of Linear Spectral Transformation for Robust Speech Recognition

Abstract

The maximum likelihood linear spectral transformation (ML-LST) using a numerical iteration method has been previously proposed for robust speech recognition. The numerical iteration method is not appropriate for real-time applications due to its computational complexity In order to reduce the computational cost, the objective function of the ML-LST is approximated and a closed-form solution is proposed in this paper It is shown experimentally that the proposed closed-form solution for the ML-LST can provide rapid speaker and environment adaptation for robust speech recognition.