Application of VTS Approximation Based Feature Compensation Approach to Speech Recognition
In the environment with various noises,the mismatch between training and testing will result in a great reduction of speech recognition performance.As an effective method for robust speech recognition,feature compensation can refine testing noisy feature to be closer to training feature.In this paper,we proposed a practical feature compensation approach based on Vector Taylor Series(VTS) approximation using explicit model of environmental distortion.Firstly,we test and verify the effectiveness of traditional offline VTS algorithm in real car environment,however this offline algorithm has a large amount of calculation,in order to make it practical,the algorithm has been improved to increase efficiency and keep the performance comparable to the offline condition.From experimental results,performance of the practical VTS algorithm proposed in the paper is much close to the best performance of the offline condition.