Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41418
Title: Hidden Logistic Linear Regression for Support Vector Machine based phone verification
Authors: Li, B.
Sim, K.C. 
Keywords: Logistic Linear Regression
Neural network
Phone verification
Support Vector Machine
Issue Date: 2010
Source: Li, B.,Sim, K.C. (2010). Hidden Logistic Linear Regression for Support Vector Machine based phone verification. Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010 : 2614-2617. ScholarBank@NUS Repository.
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.
Source Title: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/41418
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