A support vector machines-based rejection technique for speech recognition

Support vector machines represent a new approach to pattern classification developed from the theory of structural risk minimization. In this paper, we present an investigation into the application of support vector machines to the confidence measurement problem in speech recognition. Specifically, based on the results from an initial decoding of an utterance during speech recognition, we derive a feature vector consisting of parameters such as word score density, N-best word score density differences, relative word score and relative word duration as input to the confidence measurement process in which hypothetically correct utterances are accepted and utterances determined to be incorrect are rejected. We propose a new approach to training support vector machines. In this paper, we train and test a support vector machines classifier and compare the results with other statistical classification methods.