Automatic confidence measure extraction for SVM outputs using neural network

In this paper, a trainable confidence measuring system has been proposed and tested on speech recognition systems based on SVM classifiers. Classically, most of speech recognition methods have been established on the basis of probability models and statistical density estimation of each language unit and the confidence measure (CM) is extracted implicitly as a byproduct of the process of classification. Although support vector machines have shown their potential in optimizing the recognition rate, an appropriate CM has not been proposed for this purpose. This paper describes two methods to add CM into the SVM outputs using trainable intelligent systems. The first method is the simulation of Platt method using neural network and the second method is a linear combination of Platt sigmoid function using multi-layer perceptron. The experiments of these methods have been arranged on the dialects of TIMIT corpus. The results of these experiments 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/b/,/d/rdquo , ldquo/b/,/g/rdquo and ldquo/d/,g/rdquo phonemes are 6%, 3.5% and 2% 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 method.