Study on Feature Extraction and Feature Selection in Confidence Measure of Speech Recognition

Traditional speech recognition methods based on static features of a word to justify whether the word is correctly recognized or not, which neglected the information carried by its contexts and the surrounding environment.In this paper,a 14.1% word error rate (WER) speech recognizer (SR) is used as the baseline system,and 10-dimension static features achived 24.9% decline of Classification Error Rate (CER). Context features and dynamic features are extracted in relation to the static features.The total 42-dimension features get an better CER of 7.4% than static features.But not all these features have a positive impact on the classification.Too many features not only take redundant information,but also make the classification process time-consuming.To solve this problem,feature extraction which can extract prime information from original features and feature selection method which can select effective features from the original feature set are proposed in this paper.The experimental results show that context features and dynamic features are effective features for classification,and the features can be considerably compressed through feature extraction and feature selection.