Large scale speaker recognition method that uses 2D-haar acoustic feature

When we use the text-independent speaker recognition technology, the recognition accuracy degrades significantly as the number of target speakers increases. In order to improve the accuracy, a high accuracy large-scale speaker recognition method was proposed. This method combined certain number of continuous audio frames to be an acoustic feature figure, and then got the high-dimension 2D-Haar acoustic feature, which provide more probabilities to train a better classifier; AdaBoost.MH algorithm was employed to find out efficient 2D-Haar acoustic feature combination for classifier training. The experimental results show that recognition rate is 89.5% when the number of target speakers is 600, and average rate is 91.3% when the number of target speakers increases from 100 to 600. This method is suitable for large-scale speaker recognition and 2D-Haar acoustic feature is effective to yield higher performance. In addition, this method also has low algorithm complexity and time consumption.