Error corrective classifier fusion for spoken Language Recognition

A number of effective classification algorithms have been developed for spoken language recognition, and it has been a common practice in the NIST Language Recognition Evaluations (LREs) that an information fusion is applied to boost the performance of the recognition system. This paper investigates the fusion of multiple output scores generated using different classifiers that complement to further reduce the classification error rate in spoken language recognition. We introduce a local performance metric to optimize the performance of the classifier fusion. The experiments are conducted on the 2009 NIST LRE corpus. The experimental results show that the proposed fusion effectively improves the performance over individual classifiers.