An analysis of data fusion methods for speaker verification

In this paper, we analyze the diversity of information as provided by several modeling approaches for speaker verification. This information is used to facilitate the fusion of the individual results into an overall result that provides advantages in accuracy over the individual models. The modeling methods that are evaluated consist of the neural tree network (NTN), Gaussian mixture model (GMM), hidden Markov model (HMM), and dynastic time warping (DTW). With the exception of DTW, all methods utilize subword-based approaches. The phrase-level scores for each modeling approach are used for combination, Several data fusion methods are evaluated for combining the model results, including the linear and log opinion pool approaches along with voting. The results of the above analysis have been integrated into a system that has been tested with several databases collected within landline and cellular environments. We have found the Linear and log opinion pool methods to consistently reduce the error rate from that obtained when the models are need individually.