Confidence Measure Extraction for SVM Speech Classifiers Using Artificial Neural Networks
Although the recognition results of support vector machines are very promising in many applications,however there is a gap between the accuracy of SVM based speech recognizers and time series models (e.g.HMM).The main reason is the lack of reliable confidence measure (CM) in SVM outputs.This paper describes two methods to add CM into binary SVM outputs using trainable intelligent systems.The first method is the simulation of Platt method using neural network while the second method is a linear combination of Platt sigmoid functions using multilayer perceptron.The results of experiments,arranged on a set of confused phonemes using TIMIT corpus,show that the second method demonstrates better performance than the first one,e.g.After rejecting 20％ of classifications by CM,the achieved error rates for /p/,/t/ ,/p/,/q/ and /t/,q/ phonemes are 3.86％,2.1％ and 0.6％ respectively,while this error rate is much higher without employing neural networks.Although by increasing the number of phonemes,the performance of the second method will match that of the first one.
S.Amini F.Razzazi K.Nayebi
Electrical Engineering Department,Islamic Azad University,Science and Research Branch,Tehran,Iran BeenaVision Co.,GA,USA
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）