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 multi-layer 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 ldquo/p/,/t/rdquo, ldquo/p/,/q/rdquo and ldquo/t/,q/rdquo 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.