Leveraging the speaker and noise space for effective in-set/out-of-set speaker recognition.

摘要

This study addresses the problem of identifying in-set versus out-of-set speakers in noise for limited train/test duration speech segments in situations where rapid detection and tracking is required. The objective is to form a decision as to whether the current input speaker is accepted as a member of the enrolled in-set group or rejected as an outside speaker. A new scoring algorithm that combines scores across an energy-frequency grid is developed where high-energy speaker dependent frames are fused with weighted scores from low-energy noise dependent frames. By leveraging the balance between the speaker versus the background noise environment, it is possible to realize an improvement in overall equal error rate performance. Using speakers from the TIMIT database with 5 seconds of train and 2 seconds of test, the average optimum relative EER performance improvement for the proposed full selective leveraging approach is 31.6%. The results confirm that for situations in which the background environment type remains constant between train and test, an in-set/out-of-set speaker recognition system that takes advantage of information gathered from the environmental noise can be formulated which realizes significant improvement.