Hidden Logistic Linear Regression for Support Vector Machine based phone verification

Abstract

Phone verification approach to mispronunciation detection using a combination of Neural Network (NN) and Support Vector Machine (SVM) has been shown to yield improved verification performance. This approach uses a NN to predict the HMM state posterior probabilities. The average posterior probability vectors computed over each phone segment are used as input features to a SVM back-end to generate the final verification scores. In this paper, a novel Hidden Logistic Feature (HLF) for SVM back-end is proposed, where the sigmoid activations from the hidden layer that contain rich information of the NN is used instead of the output layer and the generation of HLFs can be interpreted as a Hidden Logistic Linear Regression process. Experiments on the TIMIT database show that the proposed HLF gives the lowest Equal Error Rate of 3.63%. © 2010 ISCA.